Athero-Express Biobank Study – IL6 and MCP1 plaque levels.
An ‘Athero-Express Biobank Study’ project
Sander W. van der Laan, PhD | @swvanderlaan; Marios Georgakis; Rainer Malik; Martin Dichgans
2020-06-17
Preparation
Clean the environment.
Set locations, and the working directory.
A package-installation function.
Load those packages.
We will create a datestamp and define the Utrecht Science Park Colour Scheme.
# Function to grep data from glm()/lm()
GLM.CON <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' .\n")
if (nrow(summary(fit)$coefficients) == 1) {
output = c(DATASET, x_name, y, rep(NA,8))
cat("Model not fitted; probably singular.\n")
}else {
cat("Collecting data.\n\n")
effectsize = summary(fit)$coefficients[2,1]
SE = summary(fit)$coefficients[2,2]
OReffect = exp(summary(fit)$coefficients[2,1])
CI_low = exp(effectsize - 1.96 * SE)
CI_up = exp(effectsize + 1.96 * SE)
tvalue = summary(fit)$coefficients[2,3]
pvalue = summary(fit)$coefficients[2,4]
R = summary(fit)$r.squared
R.adj = summary(fit)$adj.r.squared
sample_size = nrow(model.frame(fit))
AE_N = AEDB.CEA.samplesize
Perc_Miss = 100 - ((sample_size * 100)/AE_N)
output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, tvalue, pvalue, R, R.adj, AE_N, sample_size, Perc_Miss)
if (verbose == TRUE) {
cat("We have collected the following and summarize it in an object:\n")
cat("Dataset...................:", DATASET, "\n")
cat("Score/Exposure/biomarker..:", x_name, "\n")
cat("Trait/outcome.............:", y, "\n")
cat("Effect size...............:", round(effectsize, 6), "\n")
cat("Standard error............:", round(SE, 6), "\n")
cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
cat("T-value...................:", round(tvalue, 6), "\n")
cat("P-value...................:", signif(pvalue, 8), "\n")
cat("R^2.......................:", round(R, 6), "\n")
cat("Adjusted r^2..............:", round(R.adj, 6), "\n")
cat("Sample size of AE DB......:", AE_N, "\n")
cat("Sample size of model......:", sample_size, "\n")
cat("Missing data %............:", round(Perc_Miss, 6), "\n")
} else {
cat("Collecting data in summary object.\n")
}
}
return(output)
print(output)
}
GLM.BIN <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' ...\n")
if (nrow(summary(fit)$coefficients) == 1) {
output = c(DATASET, x_name, y, rep(NA,9))
cat("Model not fitted; probably singular.\n")
}else {
cat("Collecting data...\n")
effectsize = summary(fit)$coefficients[2,1]
SE = summary(fit)$coefficients[2,2]
OReffect = exp(summary(fit)$coefficients[2,1])
CI_low = exp(effectsize - 1.96 * SE)
CI_up = exp(effectsize + 1.96 * SE)
zvalue = summary(fit)$coefficients[2,3]
pvalue = summary(fit)$coefficients[2,4]
dev <- fit$deviance
nullDev <- fit$null.deviance
modelN <- length(fit$fitted.values)
R.l <- 1 - dev / nullDev
R.cs <- 1 - exp(-(nullDev - dev) / modelN)
R.n <- R.cs / (1 - (exp(-nullDev/modelN)))
sample_size = nrow(model.frame(fit))
AE_N = AEDB.CEA.samplesize
Perc_Miss = 100 - ((sample_size * 100)/AE_N)
output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, zvalue, pvalue, R.l, R.cs, R.n, AE_N, sample_size, Perc_Miss)
if (verbose == TRUE) {
cat("We have collected the following and summarize it in an object:\n")
cat("Dataset...................:", DATASET, "\n")
cat("Score/Exposure/biomarker..:", x_name, "\n")
cat("Trait/outcome.............:", y, "\n")
cat("Effect size...............:", round(effectsize, 6), "\n")
cat("Standard error............:", round(SE, 6), "\n")
cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
cat("Z-value...................:", round(zvalue, 6), "\n")
cat("P-value...................:", signif(pvalue, 8), "\n")
cat("Hosmer and Lemeshow r^2...:", round(R.l, 6), "\n")
cat("Cox and Snell r^2.........:", round(R.cs, 6), "\n")
cat("Nagelkerke's pseudo r^2...:", round(R.n, 6), "\n")
cat("Sample size of AE DB......:", AE_N, "\n")
cat("Sample size of model......:", sample_size, "\n")
cat("Missing data %............:", round(Perc_Miss, 6), "\n")
} else {
cat("Collecting data in summary object.\n")
}
}
return(output)
print(output)
}Background
Using a Mendelian Randomization approach, we recently examined associations between the circulating levels of 41 cytokines and growth factors and the risk of stroke in the MEGASTROKE GWAS dataset (67,000 stroke cases and 450,000 controls) and found Monocyte chemoattractant protein-1 (MCP-1) as the cytokine showing the strongest association with stroke, particularly large artery and cardioembolic stroke (Georgakis et al., 2019a). Genetically elevated MCP-1 levels were also associated with a higher risk of coronary artery disease and myocardial infarction (Georgakis et al., 2019a). Further, in a meta-analysis of 6 observational population-based of longitudinal cohort studies we recently showed that baseline levels of MCP-1 were associated with a higher risk of ischemic stroke over follow-up (Georgakis et al., 2019b). While these data suggest a central role of MCP-1 in the pathogenesis of atherosclerosis, it remains unknown if MCP-1 levels in the blood really reflect MCP-1 activity. MCP-1 is expressed in the atherosclerotic plaque and attracts monocytes in the subendothelial space (Nelken et al., 1991; Papadopoulou et al., 2008; Takeya et al., 1993; Wilcox et al., 1994). Thus, MCP-1 levels in the plaque might more strongly reflect MCP-1 signaling. However, it remains unknown if MCP-1 plaque levels associate with plaque vulnerability or risk of cardiovascular events.
Objectives
Against this background we now aim to make use of the data from Athero-Express Biobank Study to explore the associations of MCP-1 protein levels in the atherosclerotic plaques from patients undergoing carotid endarterectomy with phenotypes of plaque vulnerability and secondary vascular events over a follow-up of three years.
Methods
Blood
- IL6: Interleukin 6. Entrez Gene: 3569. Bender MedSystems; cat.nr.: BMS810FF. Recalculated FACS. [pg/mL]
- MCP1: Monocyte chemotactic protein 1, MCP-1 (Chemokine (C-C motif) ligand 2, CCL2). Entrez Gene: 6347. Measured at the WKZ. Recalculated Luminex. [pg/mL]
Plaque
- IL6: Interleuking 6 (IL6; Entrez Gene: 3569) concentration in plaque [pg/ug], measured by Luminex at the WKZ.
- MCP1: Monocyte chemotactic protein 1 (a.k.a. CCL2; Entrez Gene: 6347) concentration in plaque [pg/ug], measured by Luminex at the WKZ.
- IL6R: Interleuking 6 receptor (IL6R; Entrez Gene: 3570) concentration in plaque [pg/ug], measured by Luminex at the WKZ.
Loading data
Clinical data
Loading Athero-Express clinical data.
require(haven)
# AEDB <- haven::read_sav(paste0(AEDB_loc, "/2019-3NEW_AtheroExpressDatabase_ScientificAE_02072019_IC_added.sav"))
AEDB <- haven::read_sav(paste0(AEDB_loc, "/2020_1_NEW_AtheroExpressDatabase_ScientificAE_16-03-2020.sav"))
head(AEDB)NA
NA
NAExamine AEDB
We can examine the contents of the Athero-Express Biobank dataset to know what each variable is called, what class (type) it has, and what the variable description is.
There is an excellent post on this: https://www.r-bloggers.com/working-with-spss-labels-in-r/.
AEDB %>% sjPlot::view_df(show.type = TRUE,
show.frq = TRUE,
show.prc = TRUE,
show.na = TRUE,
max.len = TRUE,
wrap.labels = 20,
verbose = FALSE,
use.viewer = FALSE,
file = paste0(OUT_loc, "/", Today, ".AEDB.dictionary.html")) Following 3 variables have only missing values and are not shown:yearpsy5 [326], yearchol3 [347], yearablo3 [419]Fixing and creating variables
We need to be very strict in defining symptoms. Therefore we will fix a new variable that groups symptoms at inclusion.
Coding of symptoms is as follows:
- missing -999
- Asymptomatic 0
- TIA 1
- minor stroke 2
- Major stroke 3
- Amaurosis fugax 4
- Four vessel disease 5
- Vertebrobasilary TIA 7
- Retinal infarction 8
- Symptomatic, but aspecific symtoms 9
- Contralateral symptomatic occlusion 10
- retinal infarction 11
- armclaudication due to occlusion subclavian artery, CEA needed for bypass 12
- retinal infarction + TIAs 13
- Ocular ischemic syndrome 14
- ischemisch glaucoom 15
- subclavian steal syndrome 16
- TGA 17
We will group as follows in Symptoms.5G:
- Asymptomatic > 0
- TIA > 1, 7, 13
- Stroke > 2, 3
- Ocular > 4, 14, 15
- Retinal infarction > 8, 11
- Other > 5, 9, 10, 12, 16, 17
We will also group as follows in AsymptSympt:
- Asymptomatic > 0
- TIA > 1, 7, 13 + Stroke > 2, 3
- Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17
We will also group as follows in AsymptSympt2G:
- Asymptomatic > 0
- TIA > 1, 7, 13 + Stroke > 2, 3 Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17
# Fix symptoms
attach(AEDB)
AEDB$sympt[is.na(AEDB$sympt)] <- -999
# Symptoms.5G
AEDB[,"Symptoms.5G"] <- NA
# AEDB$Symptoms.5G[sympt == "NA"] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == -999] <- NA
AEDB$Symptoms.5G[sympt == 0] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == 1 | sympt == 7 | sympt == 13] <- "TIA"
AEDB$Symptoms.5G[sympt == 2 | sympt == 3] <- "Stroke"
AEDB$Symptoms.5G[sympt == 4 | sympt == 14 | sympt == 15 ] <- "Ocular"
AEDB$Symptoms.5G[sympt == 8 | sympt == 11] <- "Retinal infarction"
AEDB$Symptoms.5G[sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Other"
# AsymptSympt
AEDB[,"AsymptSympt"] <- NA
AEDB$AsymptSympt[sympt == -999] <- NA
AEDB$AsymptSympt[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3] <- "Symptomatic"
AEDB$AsymptSympt[sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Ocular and others"
# AsymptSympt
AEDB[,"AsymptSympt2G"] <- NA
AEDB$AsymptSympt2G[sympt == -999] <- NA
AEDB$AsymptSympt2G[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt2G[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3 | sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Symptomatic"
detach(AEDB)
# table(AEDB$sympt, useNA = "ifany")
# table(AEDB$AsymptSympt2G, useNA = "ifany")
# table(AEDB$Symptoms.5G, useNA = "ifany")
#
# table(AEDB$AsymptSympt2G, AEDB$sympt, useNA = "ifany")
# table(AEDB$Symptoms.5G, AEDB$sympt, useNA = "ifany")
table(AEDB$AsymptSympt2G, AEDB$Symptoms.5G, useNA = "ifany")
Asymptomatic Ocular Other Retinal infarction Stroke TIA <NA>
Asymptomatic 333 0 0 0 0 0 0
Symptomatic 0 416 119 43 732 1045 0
<NA> 0 0 0 0 0 0 1103# AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "sympt", "Symptoms.5G", "AsymptSympt"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# table(AEDB.temp$Symptoms.5G, AEDB.temp$AsymptSympt)
#
# rm(AEDB.temp)
We will also fix the plaquephenotypes variable.
Coding of symptoms is as follows:
- missing -999
- not relevant -888
- fibrous 1
- fibroatheromatous 2
- atheromatous 3
# Fix plaquephenotypes
attach(AEDB)
AEDB[,"OverallPlaquePhenotype"] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == 1] <- "fibrous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 2] <- "fibroatheromatous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 3] <- "atheromatous"
detach(AEDB)
table(AEDB$OverallPlaquePhenotype)
atheromatous fibroatheromatous fibrous
550 841 1439 # AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "plaquephenotype", "OverallPlaquePhenotype"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)
We will also fix the diabetes status variable. We define diabetes as history of a diagnosis and/or use of glucose-lowering medications.
# Fix diabetes
attach(AEDB)
AEDB[,"DiabetesStatus"] <- NA
AEDB$DiabetesStatus[DM.composite == -999] <- NA
AEDB$DiabetesStatus[DM.composite == 0] <- "Control (no Diabetes Dx/Med)"
AEDB$DiabetesStatus[DM.composite == 1] <- "Diabetes"
detach(AEDB)
table(AEDB$DM.composite)
0 1
2764 985 table(AEDB$DiabetesStatus)
Control (no Diabetes Dx/Med) Diabetes
2764 985 # AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)
We will also fix the smoking status variable. We are interested in whether someone never, ever or is currently (at the time of inclusion) smoking. This is based on the questionnaire.
diet801: are you a smoker?diet802: did you smoke in the past?
We already have some variables indicating smoking status:
SmokingReported: patient has reported to smoke.SmokingYearOR: smoking in the year of surgery?SmokerCurrent: currently smoking?
require(labelled)
AEDB$diet801 <- to_factor(AEDB$diet801)
AEDB$diet802 <- to_factor(AEDB$diet802)
AEDB$diet805 <- to_factor(AEDB$diet805)
AEDB$SmokingReported <- to_factor(AEDB$SmokingReported)
AEDB$SmokerCurrent <- to_factor(AEDB$SmokerCurrent)
AEDB$SmokingYearOR <- to_factor(AEDB$SmokingYearOR)
# table(AEDB$diet801)
# table(AEDB$diet802)
# table(AEDB$SmokingReported)
# table(AEDB$SmokerCurrent)
# table(AEDB$SmokingYearOR)
# table(AEDB$SmokingReported, AEDB$SmokerCurrent, useNA = "ifany", dnn = c("Reported smoking", "Current smoker"))
#
# table(AEDB$diet801, AEDB$diet802, useNA = "ifany", dnn = c("Smoker", "Past smoker"))
cat("\nFixing smoking status.\n")
Fixing smoking status.attach(AEDB)
AEDB[,"SmokerStatus"] <- NA
AEDB$SmokerStatus[diet802 == "don't know"] <- "Never smoked"
AEDB$SmokerStatus[diet802 == "I still smoke"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "no"] <- "Never smoked"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "yes"] <- "Ex-smoker"
AEDB$SmokerStatus[SmokerCurrent == "yes"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no data available/missing"] <- NA
# AEDB$SmokerStatus[is.na(SmokerCurrent)] <- "Never smoked"
detach(AEDB)
cat("\n* Current smoking status.\n")
* Current smoking status.table(AEDB$SmokerCurrent,
useNA = "ifany",
dnn = c("Current smoker"))Current smoker
no data available/missing no yes <NA>
0 2364 1308 119 cat("\n* Updated smoking status.\n")
* Updated smoking status.table(AEDB$SmokerStatus,
useNA = "ifany",
dnn = c("Updated smoking status"))Updated smoking status
Current smoker Ex-smoker Never smoked <NA>
1308 1814 389 280 cat("\n* Comparing to 'SmokerCurrent'.\n")
* Comparing to 'SmokerCurrent'.table(AEDB$SmokerStatus, AEDB$SmokerCurrent,
useNA = "ifany",
dnn = c("Updated smoking status", "Current smoker")) Current smoker
Updated smoking status no data available/missing no yes <NA>
Current smoker 0 0 1308 0
Ex-smoker 0 1814 0 0
Never smoked 0 389 0 0
<NA> 0 161 0 119# AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)
We will also fix the alcohol status variable.
# Fix diabetes
attach(AEDB)
AEDB[,"AlcoholUse"] <- NA
AEDB$AlcoholUse[diet810 == -999] <- NA
AEDB$AlcoholUse[diet810 == 0] <- "No"
AEDB$AlcoholUse[diet810 == 1] <- "Yes"
detach(AEDB)
table(AEDB$AlcoholUse)
No Yes
1238 2345 # AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)
We will also fix a history of CAD, stroke or peripheral intervention status variable. This will be based on CAD_history, Stroke_history, and Peripheral.interv
# Fix diabetes
attach(AEDB)
AEDB[,"MedHx_CVD"] <- NA
AEDB$MedHx_CVD[CAD_history == 0 | Stroke_history == 0 | Peripheral.interv == 0] <- "No"
AEDB$MedHx_CVD[CAD_history == 1 | Stroke_history == 1 | Peripheral.interv == 1] <- "yes"
detach(AEDB)
table(AEDB$CAD_history)
0 1
2430 1285 table(AEDB$Stroke_history)
0 1
2763 947 table(AEDB$Peripheral.interv)
0 1
2579 1099 table(AEDB$MedHx_CVD)
No yes
1309 2475 # AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)
Athero-Express Biobank Study
Baseline characteristics
We are interested in the following variables at baseline.
- Age (years)
- Female sex (N, %)
- Hypertension (N, %)
- SBP (mmHg)
- DBP (mmHg)
- Diabetes mellitus (N, %)
- Total cholesterol levels (mg/dL)
- LDL cholesterol levels (mg/dL)
- HDL cholesterol levels (mg/dL)
- Triglyceride levels (mg/dL)
- Use of statins (N, %)
- Use of antiplatelet drugs (N, %)
- BMI (kg/m²)
- Smoking status (N, %)
- Never smokers
- Ex-smokers
- Current smokers
- History of CAD (N, %)
- History of PAD (N, %)
- Clinical manifestations
- Asymptomatic
- Amaurosis fugax
- TIA
- Stroke
- eGFR (mL/min/1.73 m²)
- MCP-1 plaque levels (pg/mL)
- MCP-1 serum levels (pg/mL)
cat("===========================================================================================\n")===========================================================================================cat("CREATE BASELINE TABLE\n")CREATE BASELINE TABLE# Baseline table variables
basetable_vars = c("Hospital", "ORyear",
"Age", "Gender",
"TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU",
"TC_final", "LDL_final", "HDL_final", "TG_final",
"hsCRP_plasma",
"systolic", "diastoli", "GFR_MDRD", "BMI",
"KDOQI", "BMI_WHO",
"SmokerStatus", "AlcoholUse",
"DiabetesStatus",
"Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs",
"Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
"Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
"restenos", "stenose",
"MedHx_CVD", "CAD_history", "PAOD", "Peripheral.interv",
"EP_composite", "EP_composite_time",
"macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
"neutrophils", "Mast_cells_plaque",
"IPH.bin", "vessel_density_averaged",
"Calc.bin", "Collagen.bin",
"Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
"IL6", "IL6_pg_ug_2015", "IL6R_pg_ug_2015",
"MCP1", "MCP1_pg_ug_2015")
basetable_bin = c("Gender",
"KDOQI", "BMI_WHO",
"SmokerStatus", "AlcoholUse",
"DiabetesStatus",
"Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs",
"Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
"Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
"restenos", "stenose",
"CAD_history", "PAOD", "Peripheral.interv",
"EP_composite", "Macrophages.bin", "SMC.bin",
"IPH.bin",
"Calc.bin", "Collagen.bin",
"Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype")
# basetable_bin
basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_conShowing the baseline table of the whole Athero-Express Biobank.
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
# strata = "Symptoms.4g",
data = AEDB, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:3]
level Overall Missing
n 3791
Hospital % (freq) St. Antonius, Nieuwegein 45.8 (1735) 0.0
UMC Utrecht 54.2 (2056)
ORyear % (freq) No data available/missing 0.0 ( 0) 0.0
2002 2.5 ( 94)
2003 5.4 ( 204)
2004 7.6 ( 289)
2005 8.2 ( 309)
2006 7.5 ( 285)
2007 6.2 ( 234)
2008 5.9 ( 223)
2009 7.0 ( 267)
2010 8.1 ( 307)
2011 7.1 ( 269)
2012 8.2 ( 312)
2013 6.9 ( 262)
2014 7.9 ( 299)
2015 2.1 ( 79)
2016 3.3 ( 124)
2017 2.2 ( 85)
2018 2.1 ( 80)
2019 1.8 ( 69)
Age (mean (SD)) 68.907 (9.322) 0.0
Gender % (freq) female 30.6 (1160) 0.0
male 69.4 (2631)
TC_finalCU (mean (SD)) 185.220 (81.513) 46.8
LDL_finalCU (mean (SD)) 106.483 (40.683) 54.5
HDL_finalCU (mean (SD)) 46.593 (16.730) 51.1
TG_finalCU (mean (SD)) 154.233 (99.797) 51.8
TC_final (mean (SD)) 4.797 (2.111) 46.8
LDL_final (mean (SD)) 2.758 (1.054) 54.5
HDL_final (mean (SD)) 1.207 (0.433) 51.1
TG_final (mean (SD)) 1.743 (1.128) 51.8
hsCRP_plasma (mean (SD)) 19.250 (206.888) 60.7
systolic (mean (SD)) 150.907 (25.117) 13.5
diastoli (mean (SD)) 79.934 (21.853) 13.5
GFR_MDRD (mean (SD)) 74.849 (24.745) 6.5
BMI (mean (SD)) 26.336 (4.051) 7.5
KDOQI % (freq) No data available/missing 0.0 ( 0) 6.6
Normal kidney function 22.1 ( 839)
CKD 2 (Mild) 47.2 (1788)
CKD 3 (Moderate) 21.9 ( 830)
CKD 4 (Severe) 1.4 ( 53)
CKD 5 (Failure) 0.8 ( 32)
<NA> 6.6 ( 249)
BMI_WHO % (freq) No data available/missing 0.0 ( 0) 7.5
Underweight 1.2 ( 44)
Normal 35.2 (1335)
Overweight 42.0 (1594)
Obese 14.1 ( 533)
<NA> 7.5 ( 285)
SmokerStatus % (freq) Current smoker 34.5 (1308) 7.4
Ex-smoker 47.9 (1814)
Never smoked 10.3 ( 389)
<NA> 7.4 ( 280)
AlcoholUse % (freq) No 32.7 (1238) 5.5
Yes 61.9 (2345)
<NA> 5.5 ( 208)
DiabetesStatus % (freq) Control (no Diabetes Dx/Med) 72.9 (2764) 1.1
Diabetes 26.0 ( 985)
<NA> 1.1 ( 42)
Hypertension.selfreport % (freq) No data available/missing 0.0 ( 0) 4.0
no 23.7 ( 899)
yes 72.3 (2741)
<NA> 4.0 ( 151)
Hypertension.selfreportdrug % (freq) No data available/missing 0.0 ( 0) 5.5
no 28.6 (1085)
yes 65.9 (2499)
<NA> 5.5 ( 207)
Hypertension.composite % (freq) No data available/missing 0.0 ( 0) 1.3
no 13.3 ( 504)
yes 85.4 (3239)
<NA> 1.3 ( 48)
Hypertension.drugs % (freq) No data available/missing 0.0 ( 0) 1.5
no 21.0 ( 797)
yes 77.5 (2939)
<NA> 1.5 ( 55)
Med.anticoagulants % (freq) No data available/missing 0.0 ( 0) 1.6
no 85.6 (3246)
yes 12.8 ( 485)
<NA> 1.6 ( 60)
Med.all.antiplatelet % (freq) No data available/missing 0.0 ( 0) 1.6
no 13.7 ( 521)
yes 84.7 (3211)
<NA> 1.6 ( 59)
Med.Statin.LLD % (freq) No data available/missing 0.0 ( 0) 1.5
no 21.8 ( 826)
yes 76.7 (2909)
<NA> 1.5 ( 56)
Stroke_Dx % (freq) Missing 0.0 ( 0) 8.1
No stroke diagnosed 74.4 (2822)
Stroke diagnosed 17.5 ( 662)
<NA> 8.1 ( 307)
sympt % (freq) missing 29.1 (1103) 0.0
Asymptomatic 8.8 ( 333)
TIA 27.4 (1040)
minor stroke 12.1 ( 458)
Major stroke 7.2 ( 274)
Amaurosis fugax 10.5 ( 398)
Four vessel disease 1.1 ( 43)
Vertebrobasilary TIA 0.1 ( 5)
Retinal infarction 1.0 ( 37)
Symptomatic, but aspecific symtoms 1.6 ( 61)
Contralateral symptomatic occlusion 0.3 ( 12)
retinal infarction 0.2 ( 6)
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.0 ( 1)
retinal infarction + TIAs 0.0 ( 0)
Ocular ischemic syndrome 0.5 ( 18)
ischemisch glaucoom 0.0 ( 0)
subclavian steal syndrome 0.1 ( 2)
TGA 0.0 ( 0)
Symptoms.5G % (freq) Asymptomatic 8.8 ( 333) 29.1
Ocular 11.0 ( 416)
Other 3.1 ( 119)
Retinal infarction 1.1 ( 43)
Stroke 19.3 ( 732)
TIA 27.6 (1045)
<NA> 29.1 (1103)
AsymptSympt % (freq) Asymptomatic 8.8 ( 333) 29.1
Ocular and others 15.2 ( 578)
Symptomatic 46.9 (1777)
<NA> 29.1 (1103)
AsymptSympt2G % (freq) Asymptomatic 8.8 ( 333) 29.1
Symptomatic 62.1 (2355)
<NA> 29.1 (1103)
restenos % (freq) missing 0.0 ( 0) 4.0
de novo 87.0 (3297)
restenosis 8.8 ( 334)
stenose bij angioseal na PTCA 0.2 ( 7)
<NA> 4.0 ( 153)
stenose % (freq) missing 0.0 ( 0) 7.0
0-49% 0.7 ( 25)
50-70% 6.8 ( 256)
70-90% 35.6 (1349)
90-99% 29.9 (1132)
100% (Occlusion) 14.8 ( 560)
NA 0.1 ( 3)
50-99% 2.6 ( 99)
70-99% 2.6 ( 100)
99 0.1 ( 2)
<NA> 7.0 ( 265)
MedHx_CVD % (freq) No 34.5 (1309) 0.2
yes 65.3 (2475)
<NA> 0.2 ( 7)
CAD_history % (freq) Missing 0.0 ( 0) 2.0
No history CAD 64.1 (2430)
History CAD 33.9 (1285)
<NA> 2.0 ( 76)
PAOD % (freq) missing/no data 0.0 ( 0) 1.6
no 55.1 (2088)
yes 43.4 (1644)
<NA> 1.6 ( 59)
Peripheral.interv % (freq) no 68.0 (2579) 3.0
yes 29.0 (1099)
<NA> 3.0 ( 113)
EP_composite % (freq) No data available. 0.0 ( 0) 7.3
No composite endpoints 60.6 (2297)
Composite endpoints 32.1 (1218)
<NA> 7.3 ( 276)
EP_composite_time (mean (SD)) 2.266 (1.203) 7.4
macmean0 (mean (SD)) 0.656 (1.154) 32.4
smcmean0 (mean (SD)) 2.291 (6.620) 32.4
Macrophages.bin % (freq) no/minor 42.3 (1602) 25.7
moderate/heavy 32.0 (1215)
<NA> 25.7 ( 974)
SMC.bin % (freq) no/minor 22.9 ( 870) 25.3
moderate/heavy 51.8 (1962)
<NA> 25.3 ( 959)
neutrophils (mean (SD)) 162.985 (490.469) 91.0
Mast_cells_plaque (mean (SD)) 165.663 (163.421) 93.0
IPH.bin % (freq) no 32.3 (1223) 24.8
yes 42.9 (1628)
<NA> 24.8 ( 940)
vessel_density_averaged (mean (SD)) 8.030 (6.348) 48.0
Calc.bin % (freq) no/minor 37.9 (1437) 24.7
moderate/heavy 37.4 (1416)
<NA> 24.7 ( 938)
Collagen.bin % (freq) no/minor 14.2 ( 540) 25.2
moderate/heavy 60.6 (2297)
<NA> 25.2 ( 954)
Fat.bin_10 % (freq) <10% 32.3 (1226) 24.7
>10% 42.9 (1628)
<NA> 24.7 ( 937)
Fat.bin_40 % (freq) <40% 60.0 (2274) 24.7
>40% 15.3 ( 580)
<NA> 24.7 ( 937)
OverallPlaquePhenotype % (freq) atheromatous 14.5 ( 550) 25.3
fibroatheromatous 22.2 ( 841)
fibrous 38.0 (1439)
<NA> 25.3 ( 961)
IL6 (mean (SD)) 94.451 (278.490) 84.5
IL6_pg_ug_2015 (mean (SD)) 0.135 (0.541) 67.2
IL6R_pg_ug_2015 (mean (SD)) 0.212 (0.251) 67.1
MCP1 (mean (SD)) 130.926 (118.422) 83.7
MCP1_pg_ug_2015 (mean (SD)) 0.596 (0.880) 65.5 # Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.CEA.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
# strata = "Symptoms.4g",
data = AEDB.CEA, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:3]
level Overall Missing
n 2421
Hospital % (freq) St. Antonius, Nieuwegein 39.2 ( 948) 0.0
UMC Utrecht 60.8 (1473)
ORyear % (freq) No data available/missing 0.0 ( 0) 0.0
2002 3.3 ( 81)
2003 6.5 ( 157)
2004 7.8 ( 190)
2005 7.6 ( 185)
2006 7.6 ( 183)
2007 6.3 ( 152)
2008 5.7 ( 138)
2009 7.5 ( 181)
2010 6.6 ( 159)
2011 6.7 ( 163)
2012 7.3 ( 176)
2013 6.2 ( 149)
2014 6.7 ( 163)
2015 3.1 ( 76)
2016 3.5 ( 85)
2017 2.7 ( 65)
2018 2.7 ( 66)
2019 2.1 ( 52)
Age (mean (SD)) 69.105 (9.302) 0.0
Gender % (freq) female 30.5 ( 738) 0.0
male 69.5 (1683)
TC_finalCU (mean (SD)) 184.803 (56.262) 38.0
LDL_finalCU (mean (SD)) 108.420 (41.744) 45.6
HDL_finalCU (mean (SD)) 46.435 (17.005) 41.7
TG_finalCU (mean (SD)) 151.216 (91.277) 42.8
TC_final (mean (SD)) 4.786 (1.457) 38.0
LDL_final (mean (SD)) 2.808 (1.081) 45.6
HDL_final (mean (SD)) 1.203 (0.440) 41.7
TG_final (mean (SD)) 1.709 (1.031) 42.8
hsCRP_plasma (mean (SD)) 19.914 (231.655) 53.0
systolic (mean (SD)) 152.419 (25.166) 11.3
diastoli (mean (SD)) 81.318 (25.188) 11.3
GFR_MDRD (mean (SD)) 73.121 (21.152) 5.4
BMI (mean (SD)) 26.488 (3.977) 5.9
KDOQI % (freq) No data available/missing 0.0 ( 0) 5.5
Normal kidney function 19.1 ( 462)
CKD 2 (Mild) 50.9 (1232)
CKD 3 (Moderate) 22.8 ( 553)
CKD 4 (Severe) 1.3 ( 32)
CKD 5 (Failure) 0.4 ( 10)
<NA> 5.5 ( 132)
BMI_WHO % (freq) No data available/missing 0.0 ( 0) 5.9
Underweight 1.0 ( 24)
Normal 35.1 ( 850)
Overweight 43.4 (1051)
Obese 14.5 ( 352)
<NA> 5.9 ( 144)
SmokerStatus % (freq) Current smoker 33.2 ( 803) 5.9
Ex-smoker 48.0 (1163)
Never smoked 12.9 ( 313)
<NA> 5.9 ( 142)
AlcoholUse % (freq) No 34.5 ( 835) 4.0
Yes 61.5 (1488)
<NA> 4.0 ( 98)
DiabetesStatus % (freq) Control (no Diabetes Dx/Med) 75.2 (1820) 1.1
Diabetes 23.7 ( 574)
<NA> 1.1 ( 27)
Hypertension.selfreport % (freq) No data available/missing 0.0 ( 0) 3.2
no 24.3 ( 589)
yes 72.4 (1754)
<NA> 3.2 ( 78)
Hypertension.selfreportdrug % (freq) No data available/missing 0.0 ( 0) 4.4
no 29.9 ( 725)
yes 65.6 (1589)
<NA> 4.4 ( 107)
Hypertension.composite % (freq) No data available/missing 0.0 ( 0) 1.2
no 14.6 ( 353)
yes 84.3 (2040)
<NA> 1.2 ( 28)
Hypertension.drugs % (freq) No data available/missing 0.0 ( 0) 1.4
no 23.3 ( 565)
yes 75.3 (1823)
<NA> 1.4 ( 33)
Med.anticoagulants % (freq) No data available/missing 0.0 ( 0) 1.6
no 87.3 (2114)
yes 11.1 ( 269)
<NA> 1.6 ( 38)
Med.all.antiplatelet % (freq) No data available/missing 0.0 ( 0) 1.5
no 12.2 ( 295)
yes 86.3 (2090)
<NA> 1.5 ( 36)
Med.Statin.LLD % (freq) No data available/missing 0.0 ( 0) 1.4
no 20.3 ( 491)
yes 78.3 (1896)
<NA> 1.4 ( 34)
Stroke_Dx % (freq) Missing 0.0 ( 0) 6.9
No stroke diagnosed 71.5 (1731)
Stroke diagnosed 21.6 ( 524)
<NA> 6.9 ( 166)
sympt % (freq) missing 0.0 ( 0) 0.0
Asymptomatic 11.2 ( 270)
TIA 39.7 ( 961)
minor stroke 16.8 ( 407)
Major stroke 9.8 ( 238)
Amaurosis fugax 15.7 ( 379)
Four vessel disease 1.6 ( 38)
Vertebrobasilary TIA 0.2 ( 5)
Retinal infarction 1.4 ( 34)
Symptomatic, but aspecific symtoms 2.2 ( 53)
Contralateral symptomatic occlusion 0.5 ( 11)
retinal infarction 0.2 ( 6)
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.0 ( 1)
retinal infarction + TIAs 0.0 ( 0)
Ocular ischemic syndrome 0.7 ( 16)
ischemisch glaucoom 0.0 ( 0)
subclavian steal syndrome 0.1 ( 2)
TGA 0.0 ( 0)
Symptoms.5G % (freq) Asymptomatic 11.2 ( 270) 0.0
Ocular 16.3 ( 395)
Other 4.3 ( 105)
Retinal infarction 1.7 ( 40)
Stroke 26.6 ( 645)
TIA 39.9 ( 966)
AsymptSympt % (freq) Asymptomatic 11.2 ( 270) 0.0
Ocular and others 22.3 ( 540)
Symptomatic 66.5 (1611)
AsymptSympt2G % (freq) Asymptomatic 11.2 ( 270) 0.0
Symptomatic 88.8 (2151)
restenos % (freq) missing 0.0 ( 0) 1.4
de novo 93.7 (2268)
restenosis 4.9 ( 118)
stenose bij angioseal na PTCA 0.0 ( 0)
<NA> 1.4 ( 35)
stenose % (freq) missing 0.0 ( 0) 2.0
0-49% 0.5 ( 13)
50-70% 7.8 ( 189)
70-90% 46.6 (1127)
90-99% 38.3 ( 927)
100% (Occlusion) 1.3 ( 31)
NA 0.0 ( 1)
50-99% 0.6 ( 15)
70-99% 2.8 ( 68)
99 0.1 ( 2)
<NA> 2.0 ( 48)
MedHx_CVD % (freq) No 36.8 ( 892) 0.0
yes 63.2 (1529)
CAD_history % (freq) Missing 0.0 ( 0) 1.9
No history CAD 66.8 (1618)
History CAD 31.2 ( 756)
<NA> 1.9 ( 47)
PAOD % (freq) missing/no data 0.0 ( 0) 2.0
no 77.5 (1876)
yes 20.5 ( 497)
<NA> 2.0 ( 48)
Peripheral.interv % (freq) no 77.2 (1868) 2.9
yes 19.9 ( 482)
<NA> 2.9 ( 71)
EP_composite % (freq) No data available. 0.0 ( 0) 5.0
No composite endpoints 70.6 (1709)
Composite endpoints 24.4 ( 590)
<NA> 5.0 ( 122)
EP_composite_time (mean (SD)) 2.479 (1.109) 5.2
macmean0 (mean (SD)) 0.768 (1.184) 29.7
smcmean0 (mean (SD)) 1.985 (2.381) 29.9
Macrophages.bin % (freq) no/minor 34.9 ( 846) 24.1
moderate/heavy 40.9 ( 991)
<NA> 24.1 ( 584)
SMC.bin % (freq) no/minor 24.9 ( 602) 23.8
moderate/heavy 51.3 (1242)
<NA> 23.8 ( 577)
neutrophils (mean (SD)) 147.151 (419.998) 87.4
Mast_cells_plaque (mean (SD)) 164.488 (163.771) 90.0
IPH.bin % (freq) no 30.7 ( 744) 23.5
yes 45.8 (1108)
<NA> 23.5 ( 569)
vessel_density_averaged (mean (SD)) 8.318 (6.388) 35.1
Calc.bin % (freq) no/minor 41.6 (1006) 23.4
moderate/heavy 35.1 ( 849)
<NA> 23.4 ( 566)
Collagen.bin % (freq) no/minor 15.8 ( 382) 23.6
moderate/heavy 60.6 (1467)
<NA> 23.6 ( 572)
Fat.bin_10 % (freq) <10% 22.4 ( 542) 23.3
>10% 54.3 (1314)
<NA> 23.3 ( 565)
Fat.bin_40 % (freq) <40% 56.2 (1360) 23.3
>40% 20.5 ( 496)
<NA> 23.3 ( 565)
OverallPlaquePhenotype % (freq) atheromatous 19.8 ( 480) 23.7
fibroatheromatous 27.8 ( 672)
fibrous 28.7 ( 695)
<NA> 23.7 ( 574)
IL6 (mean (SD)) 98.812 (292.457) 78.2
IL6_pg_ug_2015 (mean (SD)) 0.138 (0.556) 52.5
IL6R_pg_ug_2015 (mean (SD)) 0.212 (0.251) 52.4
MCP1 (mean (SD)) 135.763 (120.028) 76.7
MCP1_pg_ug_2015 (mean (SD)) 0.612 (0.905) 50.6 AEDB.CEA.subset <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015))
AEDB.CEA.subset.AsymptSympt.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
strata = "AsymptSympt2G",
data = AEDB.CEA.subset, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:6] Stratified by AsymptSympt2G
level Asymptomatic Symptomatic p test Missing
n 131 1065
Hospital % (freq) St. Antonius, Nieuwegein 50.4 ( 66) 46.6 ( 496) 0.464 0.0
UMC Utrecht 49.6 ( 65) 53.4 ( 569)
ORyear % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
2002 10.7 ( 14) 3.9 ( 42)
2003 7.6 ( 10) 9.4 ( 100)
2004 17.6 ( 23) 11.5 ( 123)
2005 9.9 ( 13) 11.2 ( 119)
2006 10.7 ( 14) 10.2 ( 109)
2007 11.5 ( 15) 10.5 ( 112)
2008 7.6 ( 10) 7.4 ( 79)
2009 7.6 ( 10) 8.4 ( 89)
2010 5.3 ( 7) 7.6 ( 81)
2011 6.1 ( 8) 9.5 ( 101)
2012 5.3 ( 7) 8.3 ( 88)
2013 0.0 ( 0) 2.0 ( 21)
2014 0.0 ( 0) 0.1 ( 1)
2015 0.0 ( 0) 0.0 ( 0)
2016 0.0 ( 0) 0.0 ( 0)
2017 0.0 ( 0) 0.0 ( 0)
2018 0.0 ( 0) 0.0 ( 0)
2019 0.0 ( 0) 0.0 ( 0)
Age (mean (SD)) 66.237 (9.184) 68.941 (9.119) 0.001 0.0
Gender % (freq) female 23.7 ( 31) 31.4 ( 334) 0.088 0.0
male 76.3 (100) 68.6 ( 731)
TC_finalCU (mean (SD)) 175.987 (47.184) 183.420 (48.377) 0.180 33.4
LDL_finalCU (mean (SD)) 102.781 (38.324) 109.247 (41.008) 0.191 39.6
HDL_finalCU (mean (SD)) 43.701 (14.754) 45.814 (18.526) 0.317 36.4
TG_finalCU (mean (SD)) 157.650 (89.246) 145.238 (84.872) 0.211 36.0
TC_final (mean (SD)) 4.558 (1.222) 4.751 (1.253) 0.180 33.4
LDL_final (mean (SD)) 2.662 (0.993) 2.829 (1.062) 0.191 39.6
HDL_final (mean (SD)) 1.132 (0.382) 1.187 (0.480) 0.317 36.4
TG_final (mean (SD)) 1.781 (1.008) 1.641 (0.959) 0.211 36.0
hsCRP_plasma (mean (SD)) 5.688 (19.440) 16.588 (113.882) 0.379 38.8
systolic (mean (SD)) 153.577 (24.327) 155.822 (26.180) 0.390 14.0
diastoli (mean (SD)) 80.622 (13.225) 82.894 (13.581) 0.095 14.0
GFR_MDRD (mean (SD)) 71.026 (20.424) 71.879 (20.071) 0.653 3.5
BMI (mean (SD)) 26.623 (3.391) 26.321 (3.748) 0.383 4.2
KDOQI % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 3.6
Normal kidney function 17.6 ( 23) 17.3 ( 184)
CKD 2 (Mild) 49.6 ( 65) 53.2 ( 567)
CKD 3 (Moderate) 28.2 ( 37) 24.3 ( 259)
CKD 4 (Severe) 0.0 ( 0) 1.2 ( 13)
CKD 5 (Failure) 0.8 ( 1) 0.4 ( 4)
<NA> 3.8 ( 5) 3.6 ( 38)
BMI_WHO % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 4.3
Underweight 0.8 ( 1) 0.9 ( 10)
Normal 32.8 ( 43) 35.6 ( 379)
Overweight 51.1 ( 67) 46.1 ( 491)
Obese 13.0 ( 17) 12.8 ( 136)
<NA> 2.3 ( 3) 4.6 ( 49)
SmokerStatus % (freq) Current smoker 30.5 ( 40) 36.2 ( 385) 0.077 3.8
Ex-smoker 57.3 ( 75) 45.6 ( 486)
Never smoked 9.9 ( 13) 14.3 ( 152)
<NA> 2.3 ( 3) 3.9 ( 42)
AlcoholUse % (freq) No 38.2 ( 50) 33.3 ( 355) 0.359 4.0
Yes 59.5 ( 78) 62.4 ( 665)
<NA> 2.3 ( 3) 4.2 ( 45)
DiabetesStatus % (freq) Control (no Diabetes Dx/Med) 76.3 (100) 77.4 ( 824) 0.876 0.0
Diabetes 23.7 ( 31) 22.6 ( 241)
Hypertension.selfreport % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 2.0
no 23.7 ( 31) 26.6 ( 283)
yes 75.6 ( 99) 71.3 ( 759)
<NA> 0.8 ( 1) 2.2 ( 23)
Hypertension.selfreportdrug % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 2.7
no 30.5 ( 40) 32.9 ( 350)
yes 67.9 ( 89) 64.3 ( 685)
<NA> 1.5 ( 2) 2.8 ( 30)
Hypertension.composite % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
no 9.9 ( 13) 14.3 ( 152)
yes 90.1 (118) 85.7 ( 913)
Hypertension.drugs % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.2
no 14.5 ( 19) 23.3 ( 248)
yes 85.5 (112) 76.5 ( 815)
<NA> 0.0 ( 0) 0.2 ( 2)
Med.anticoagulants % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.2
no 89.3 (117) 88.0 ( 937)
yes 10.7 ( 14) 11.8 ( 126)
<NA> 0.0 ( 0) 0.2 ( 2)
Med.all.antiplatelet % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.4
no 6.1 ( 8) 11.0 ( 117)
yes 93.1 (122) 88.6 ( 944)
<NA> 0.8 ( 1) 0.4 ( 4)
Med.Statin.LLD % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.2
no 15.3 ( 20) 22.7 ( 242)
yes 84.7 (111) 77.1 ( 821)
<NA> 0.0 ( 0) 0.2 ( 2)
Stroke_Dx % (freq) Missing 0.0 ( 0) 0.0 ( 0) NaN 5.4
No stroke diagnosed 80.2 (105) 75.2 ( 801)
Stroke diagnosed 14.5 ( 19) 19.4 ( 207)
<NA> 5.3 ( 7) 5.4 ( 57)
sympt % (freq) missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
Asymptomatic 100.0 (131) 0.0 ( 0)
TIA 0.0 ( 0) 46.4 ( 494)
minor stroke 0.0 ( 0) 16.7 ( 178)
Major stroke 0.0 ( 0) 12.2 ( 130)
Amaurosis fugax 0.0 ( 0) 17.2 ( 183)
Four vessel disease 0.0 ( 0) 2.2 ( 23)
Vertebrobasilary TIA 0.0 ( 0) 0.2 ( 2)
Retinal infarction 0.0 ( 0) 1.4 ( 15)
Symptomatic, but aspecific symtoms 0.0 ( 0) 2.7 ( 29)
Contralateral symptomatic occlusion 0.0 ( 0) 0.6 ( 6)
retinal infarction 0.0 ( 0) 0.3 ( 3)
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.0 ( 0) 0.1 ( 1)
retinal infarction + TIAs 0.0 ( 0) 0.0 ( 0)
Ocular ischemic syndrome 0.0 ( 0) 0.1 ( 1)
ischemisch glaucoom 0.0 ( 0) 0.0 ( 0)
subclavian steal syndrome 0.0 ( 0) 0.0 ( 0)
TGA 0.0 ( 0) 0.0 ( 0)
Symptoms.5G % (freq) Asymptomatic 100.0 (131) 0.0 ( 0) <0.001 0.0
Ocular 0.0 ( 0) 17.3 ( 184)
Other 0.0 ( 0) 5.5 ( 59)
Retinal infarction 0.0 ( 0) 1.7 ( 18)
Stroke 0.0 ( 0) 28.9 ( 308)
TIA 0.0 ( 0) 46.6 ( 496)
AsymptSympt % (freq) Asymptomatic 100.0 (131) 0.0 ( 0) <0.001 0.0
Ocular and others 0.0 ( 0) 24.5 ( 261)
Symptomatic 0.0 ( 0) 75.5 ( 804)
AsymptSympt2G % (freq) Asymptomatic 100.0 (131) 0.0 ( 0) <0.001 0.0
Symptomatic 0.0 ( 0) 100.0 (1065)
restenos % (freq) missing 0.0 ( 0) 0.0 ( 0) NaN 2.3
de novo 93.9 (123) 94.7 (1009)
restenosis 2.3 ( 3) 3.2 ( 34)
stenose bij angioseal na PTCA 0.0 ( 0) 0.0 ( 0)
<NA> 3.8 ( 5) 2.1 ( 22)
stenose % (freq) missing 0.0 ( 0) 0.0 ( 0) NaN 3.2
0-49% 0.0 ( 0) 0.6 ( 6)
50-70% 3.1 ( 4) 6.4 ( 68)
70-90% 51.1 ( 67) 44.6 ( 475)
90-99% 41.2 ( 54) 42.7 ( 455)
100% (Occlusion) 0.0 ( 0) 0.9 ( 10)
NA 0.0 ( 0) 0.0 ( 0)
50-99% 0.8 ( 1) 0.4 ( 4)
70-99% 0.0 ( 0) 1.3 ( 14)
99 0.0 ( 0) 0.0 ( 0)
<NA> 3.8 ( 5) 3.1 ( 33)
MedHx_CVD % (freq) No 38.9 ( 51) 36.9 ( 393) 0.720 0.0
yes 61.1 ( 80) 63.1 ( 672)
CAD_history % (freq) Missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
No history CAD 61.8 ( 81) 69.9 ( 744)
History CAD 38.2 ( 50) 30.1 ( 321)
PAOD % (freq) missing/no data 0.0 ( 0) 0.0 ( 0) NaN 0.0
no 74.0 ( 97) 79.5 ( 847)
yes 26.0 ( 34) 20.5 ( 218)
Peripheral.interv % (freq) no 74.0 ( 97) 82.5 ( 879) 0.042 0.3
yes 26.0 ( 34) 17.2 ( 183)
<NA> 0.0 ( 0) 0.3 ( 3)
EP_composite % (freq) No data available. 0.0 ( 0) 0.0 ( 0) NaN 0.8
No composite endpoints 67.2 ( 88) 74.3 ( 791)
Composite endpoints 32.8 ( 43) 24.9 ( 265)
<NA> 0.0 ( 0) 0.8 ( 9)
EP_composite_time (mean (SD)) 2.614 (0.931) 2.613 (1.095) 0.992 0.9
macmean0 (mean (SD)) 0.837 (1.088) 0.781 (1.231) 0.623 2.3
smcmean0 (mean (SD)) 2.152 (1.861) 1.904 (2.222) 0.223 2.7
Macrophages.bin % (freq) no/minor 48.9 ( 64) 47.4 ( 505) 0.583 1.9
moderate/heavy 50.4 ( 66) 50.5 ( 538)
<NA> 0.8 ( 1) 2.1 ( 22)
SMC.bin % (freq) no/minor 22.9 ( 30) 32.2 ( 343) 0.085 1.8
moderate/heavy 75.6 ( 99) 65.9 ( 702)
<NA> 1.5 ( 2) 1.9 ( 20)
neutrophils (mean (SD)) 157.643 (507.380) 172.872 (477.038) 0.876 81.9
Mast_cells_plaque (mean (SD)) 111.400 (112.037) 183.284 (180.156) 0.056 86.1
IPH.bin % (freq) no 41.2 ( 54) 37.9 ( 404) 0.561 1.7
yes 58.0 ( 76) 60.3 ( 642)
<NA> 0.8 ( 1) 1.8 ( 19)
vessel_density_averaged (mean (SD)) 8.608 (6.547) 8.408 (6.469) 0.750 8.7
[ reached getOption("max.print") -- omitted 21 rows ]
AEDB.CEA.subset.combo <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015) | !is.na(MCP1))
AEDB.CEA.subset.combo.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
strata = "AsymptSympt2G",
data = AEDB.CEA.subset.combo, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:6] Stratified by AsymptSympt2G
level Asymptomatic Symptomatic p test Missing
n 161 1165
Hospital % (freq) St. Antonius, Nieuwegein 52.2 ( 84) 47.0 ( 547) 0.246 0.0
UMC Utrecht 47.8 ( 77) 53.0 ( 618)
ORyear % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
2002 10.6 ( 17) 4.8 ( 56)
2003 11.8 ( 19) 10.6 ( 124)
2004 19.9 ( 32) 12.2 ( 142)
2005 13.7 ( 22) 13.3 ( 155)
2006 8.7 ( 14) 10.0 ( 116)
2007 9.3 ( 15) 9.6 ( 112)
2008 6.2 ( 10) 6.8 ( 79)
2009 6.2 ( 10) 7.6 ( 89)
2010 4.3 ( 7) 7.0 ( 81)
2011 5.0 ( 8) 8.7 ( 101)
2012 4.3 ( 7) 7.6 ( 88)
2013 0.0 ( 0) 1.8 ( 21)
2014 0.0 ( 0) 0.1 ( 1)
2015 0.0 ( 0) 0.0 ( 0)
2016 0.0 ( 0) 0.0 ( 0)
2017 0.0 ( 0) 0.0 ( 0)
2018 0.0 ( 0) 0.0 ( 0)
2019 0.0 ( 0) 0.0 ( 0)
Age (mean (SD)) 65.901 (9.051) 68.788 (9.081) <0.001 0.0
Gender % (freq) female 23.0 ( 37) 30.4 ( 354) 0.066 0.0
male 77.0 (124) 69.6 ( 811)
TC_finalCU (mean (SD)) 179.199 (45.274) 183.983 (48.290) 0.331 32.7
LDL_finalCU (mean (SD)) 104.132 (37.590) 109.642 (41.227) 0.215 39.8
HDL_finalCU (mean (SD)) 44.749 (14.890) 45.808 (18.231) 0.568 36.1
TG_finalCU (mean (SD)) 158.699 (87.584) 145.942 (83.223) 0.143 35.6
TC_final (mean (SD)) 4.641 (1.173) 4.765 (1.251) 0.331 32.7
LDL_final (mean (SD)) 2.697 (0.974) 2.840 (1.068) 0.215 39.8
HDL_final (mean (SD)) 1.159 (0.386) 1.186 (0.472) 0.568 36.1
TG_final (mean (SD)) 1.793 (0.990) 1.649 (0.940) 0.143 35.6
hsCRP_plasma (mean (SD)) 6.846 (21.838) 16.213 (110.899) 0.393 40.6
systolic (mean (SD)) 152.838 (24.600) 155.742 (26.411) 0.225 13.5
diastoli (mean (SD)) 80.824 (12.855) 82.873 (13.549) 0.096 13.5
GFR_MDRD (mean (SD)) 70.440 (19.793) 71.901 (20.142) 0.396 3.5
BMI (mean (SD)) 26.626 (3.572) 26.350 (3.768) 0.389 4.4
KDOQI % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 3.5
Normal kidney function 14.9 ( 24) 17.4 ( 203)
CKD 2 (Mild) 50.9 ( 82) 53.4 ( 622)
CKD 3 (Moderate) 29.8 ( 48) 23.9 ( 279)
CKD 4 (Severe) 0.0 ( 0) 1.3 ( 15)
CKD 5 (Failure) 0.6 ( 1) 0.4 ( 5)
<NA> 3.7 ( 6) 3.5 ( 41)
BMI_WHO % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 4.6
Underweight 1.2 ( 2) 0.9 ( 11)
Normal 32.3 ( 52) 35.5 ( 414)
Overweight 49.7 ( 80) 45.6 ( 531)
Obese 13.7 ( 22) 13.1 ( 153)
<NA> 3.1 ( 5) 4.8 ( 56)
SmokerStatus % (freq) Current smoker 29.2 ( 47) 36.0 ( 419) 0.072 4.0
Ex-smoker 56.5 ( 91) 45.7 ( 532)
Never smoked 11.8 ( 19) 14.2 ( 165)
<NA> 2.5 ( 4) 4.2 ( 49)
AlcoholUse % (freq) No 38.5 ( 62) 33.6 ( 392) 0.223 3.8
Yes 59.6 ( 96) 62.2 ( 725)
<NA> 1.9 ( 3) 4.1 ( 48)
DiabetesStatus % (freq) Control (no Diabetes Dx/Med) 78.3 (126) 77.3 ( 900) 0.852 0.0
Diabetes 21.7 ( 35) 22.7 ( 265)
Hypertension.selfreport % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 1.9
no 25.5 ( 41) 26.5 ( 309)
yes 73.9 (119) 71.4 ( 832)
<NA> 0.6 ( 1) 2.1 ( 24)
Hypertension.selfreportdrug % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 2.4
no 32.3 ( 52) 32.9 ( 383)
yes 66.5 (107) 64.5 ( 752)
<NA> 1.2 ( 2) 2.6 ( 30)
Hypertension.composite % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
no 11.2 ( 18) 14.1 ( 164)
yes 88.8 (143) 85.9 (1001)
Hypertension.drugs % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.2
no 15.5 ( 25) 22.7 ( 265)
yes 83.9 (135) 77.1 ( 898)
<NA> 0.6 ( 1) 0.2 ( 2)
Med.anticoagulants % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.2
no 89.4 (144) 88.0 (1025)
yes 9.9 ( 16) 11.8 ( 138)
<NA> 0.6 ( 1) 0.2 ( 2)
Med.all.antiplatelet % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.5
no 6.2 ( 10) 10.8 ( 126)
yes 92.5 (149) 88.8 (1035)
<NA> 1.2 ( 2) 0.3 ( 4)
Med.Statin.LLD % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.2
no 17.4 ( 28) 23.2 ( 270)
yes 82.0 (132) 76.7 ( 893)
<NA> 0.6 ( 1) 0.2 ( 2)
Stroke_Dx % (freq) Missing 0.0 ( 0) 0.0 ( 0) NaN 5.5
No stroke diagnosed 80.1 (129) 75.5 ( 880)
Stroke diagnosed 13.7 ( 22) 19.1 ( 222)
<NA> 6.2 ( 10) 5.4 ( 63)
sympt % (freq) missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
Asymptomatic 100.0 (161) 0.0 ( 0)
TIA 0.0 ( 0) 46.6 ( 543)
minor stroke 0.0 ( 0) 17.2 ( 200)
Major stroke 0.0 ( 0) 11.6 ( 135)
Amaurosis fugax 0.0 ( 0) 16.9 ( 197)
Four vessel disease 0.0 ( 0) 2.1 ( 25)
Vertebrobasilary TIA 0.0 ( 0) 0.2 ( 2)
Retinal infarction 0.0 ( 0) 1.4 ( 16)
Symptomatic, but aspecific symtoms 0.0 ( 0) 3.1 ( 36)
Contralateral symptomatic occlusion 0.0 ( 0) 0.5 ( 6)
retinal infarction 0.0 ( 0) 0.3 ( 3)
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.0 ( 0) 0.1 ( 1)
retinal infarction + TIAs 0.0 ( 0) 0.0 ( 0)
Ocular ischemic syndrome 0.0 ( 0) 0.1 ( 1)
ischemisch glaucoom 0.0 ( 0) 0.0 ( 0)
subclavian steal syndrome 0.0 ( 0) 0.0 ( 0)
TGA 0.0 ( 0) 0.0 ( 0)
Symptoms.5G % (freq) Asymptomatic 100.0 (161) 0.0 ( 0) <0.001 0.0
Ocular 0.0 ( 0) 17.0 ( 198)
Other 0.0 ( 0) 5.8 ( 68)
Retinal infarction 0.0 ( 0) 1.6 ( 19)
Stroke 0.0 ( 0) 28.8 ( 335)
TIA 0.0 ( 0) 46.8 ( 545)
AsymptSympt % (freq) Asymptomatic 100.0 (161) 0.0 ( 0) <0.001 0.0
Ocular and others 0.0 ( 0) 24.5 ( 285)
Symptomatic 0.0 ( 0) 75.5 ( 880)
AsymptSympt2G % (freq) Asymptomatic 100.0 (161) 0.0 ( 0) <0.001 0.0
Symptomatic 0.0 ( 0) 100.0 (1165)
restenos % (freq) missing 0.0 ( 0) 0.0 ( 0) NaN 2.0
de novo 93.2 (150) 95.0 (1107)
restenosis 3.7 ( 6) 3.1 ( 36)
stenose bij angioseal na PTCA 0.0 ( 0) 0.0 ( 0)
<NA> 3.1 ( 5) 1.9 ( 22)
stenose % (freq) missing 0.0 ( 0) 0.0 ( 0) NaN 2.9
0-49% 0.0 ( 0) 0.6 ( 7)
50-70% 2.5 ( 4) 6.2 ( 72)
70-90% 50.9 ( 82) 44.6 ( 520)
90-99% 42.9 ( 69) 43.3 ( 504)
100% (Occlusion) 0.0 ( 0) 0.9 ( 11)
NA 0.0 ( 0) 0.0 ( 0)
50-99% 0.6 ( 1) 0.3 ( 4)
70-99% 0.0 ( 0) 1.2 ( 14)
99 0.0 ( 0) 0.0 ( 0)
<NA> 3.1 ( 5) 2.8 ( 33)
MedHx_CVD % (freq) No 37.3 ( 60) 36.7 ( 428) 0.965 0.0
yes 62.7 (101) 63.3 ( 737)
CAD_history % (freq) Missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
No history CAD 59.0 ( 95) 69.1 ( 805)
History CAD 41.0 ( 66) 30.9 ( 360)
PAOD % (freq) missing/no data 0.0 ( 0) 0.0 ( 0) NaN 0.0
no 73.9 (119) 79.8 ( 930)
yes 26.1 ( 42) 20.2 ( 235)
Peripheral.interv % (freq) no 72.7 (117) 83.0 ( 967) 0.004 0.2
yes 27.3 ( 44) 16.7 ( 195)
<NA> 0.0 ( 0) 0.3 ( 3)
EP_composite % (freq) No data available. 0.0 ( 0) 0.0 ( 0) NaN 0.8
No composite endpoints 68.3 (110) 73.8 ( 860)
Composite endpoints 31.7 ( 51) 25.2 ( 294)
<NA> 0.0 ( 0) 0.9 ( 11)
EP_composite_time (mean (SD)) 2.579 (0.961) 2.611 (1.130) 0.735 1.0
macmean0 (mean (SD)) 0.802 (1.072) 0.822 (1.275) 0.856 2.2
smcmean0 (mean (SD)) 2.445 (2.594) 1.923 (2.234) 0.007 2.5
Macrophages.bin % (freq) no/minor 50.3 ( 81) 45.8 ( 533) 0.309 1.8
moderate/heavy 49.1 ( 79) 52.3 ( 609)
<NA> 0.6 ( 1) 2.0 ( 23)
SMC.bin % (freq) no/minor 21.7 ( 35) 32.5 ( 379) 0.017 1.7
moderate/heavy 77.0 (124) 65.8 ( 766)
<NA> 1.2 ( 2) 1.7 ( 20)
neutrophils (mean (SD)) 133.447 (437.032) 158.140 (448.512) 0.754 80.9
Mast_cells_plaque (mean (SD)) 123.389 (135.924) 173.244 (168.601) 0.097 83.7
IPH.bin % (freq) no 39.1 ( 63) 36.3 ( 423) 0.512 1.5
yes 60.2 ( 97) 62.1 ( 723)
<NA> 0.6 ( 1) 1.6 ( 19)
vessel_density_averaged (mean (SD)) 8.837 (6.727) 8.439 (6.394) 0.480 8.0
[ reached getOption("max.print") -- omitted 21 rows ]AEDB.CEA.subset.serum <- subset(AEDB.CEA, !is.na(MCP1))
AEDB.CEA.subset.serum.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
strata = "AsymptSympt2G",
data = AEDB.CEA.subset.serum, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:6] Stratified by AsymptSympt2G
level Asymptomatic Symptomatic p test Missing
n 90 475
Hospital % (freq) St. Antonius, Nieuwegein 64.4 (58) 64.6 (307) 1.000 0.0
UMC Utrecht 35.6 (32) 35.4 (168)
ORyear % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
2002 17.8 (16) 10.7 ( 51)
2003 21.1 (19) 24.8 (118)
2004 33.3 (30) 29.5 (140)
2005 24.4 (22) 30.1 (143)
2006 3.3 ( 3) 4.8 ( 23)
2007 0.0 ( 0) 0.0 ( 0)
2008 0.0 ( 0) 0.0 ( 0)
2009 0.0 ( 0) 0.0 ( 0)
2010 0.0 ( 0) 0.0 ( 0)
2011 0.0 ( 0) 0.0 ( 0)
2012 0.0 ( 0) 0.0 ( 0)
2013 0.0 ( 0) 0.0 ( 0)
2014 0.0 ( 0) 0.0 ( 0)
2015 0.0 ( 0) 0.0 ( 0)
2016 0.0 ( 0) 0.0 ( 0)
2017 0.0 ( 0) 0.0 ( 0)
2018 0.0 ( 0) 0.0 ( 0)
2019 0.0 ( 0) 0.0 ( 0)
Age (mean (SD)) 66.222 (8.373) 67.646 (8.771) 0.155 0.0
Gender % (freq) female 26.7 (24) 29.1 (138) 0.740 0.0
male 73.3 (66) 70.9 (337)
TC_finalCU (mean (SD)) 186.066 (42.922) 192.279 (48.524) 0.311 18.9
LDL_finalCU (mean (SD)) 106.604 (34.040) 114.961 (42.632) 0.132 29.9
HDL_finalCU (mean (SD)) 45.880 (15.344) 46.220 (15.334) 0.865 24.8
TG_finalCU (mean (SD)) 165.992 (91.899) 157.757 (81.383) 0.446 23.2
TC_final (mean (SD)) 4.819 (1.112) 4.980 (1.257) 0.311 18.9
LDL_final (mean (SD)) 2.761 (0.882) 2.977 (1.104) 0.132 29.9
HDL_final (mean (SD)) 1.188 (0.397) 1.197 (0.397) 0.865 24.8
TG_final (mean (SD)) 1.876 (1.038) 1.783 (0.920) 0.446 23.2
hsCRP_plasma (mean (SD)) 10.234 (28.646) 17.174 (90.088) 0.563 45.3
systolic (mean (SD)) 155.889 (24.649) 157.422 (26.508) 0.628 6.0
diastoli (mean (SD)) 82.444 (10.532) 83.760 (12.877) 0.386 6.0
GFR_MDRD (mean (SD)) 68.229 (18.858) 70.909 (18.985) 0.224 1.6
BMI (mean (SD)) 26.618 (3.519) 26.497 (3.923) 0.789 6.7
KDOQI % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 1.8
Normal kidney function 11.1 (10) 15.2 ( 72)
CKD 2 (Mild) 52.2 (47) 57.3 (272)
CKD 3 (Moderate) 33.3 (30) 24.4 (116)
CKD 4 (Severe) 0.0 ( 0) 1.1 ( 5)
CKD 5 (Failure) 1.1 ( 1) 0.4 ( 2)
<NA> 2.2 ( 2) 1.7 ( 8)
BMI_WHO % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 6.9
Underweight 1.1 ( 1) 1.5 ( 7)
Normal 28.9 (26) 33.7 (160)
Overweight 52.2 (47) 43.8 (208)
Obese 13.3 (12) 13.7 ( 65)
<NA> 4.4 ( 4) 7.4 ( 35)
SmokerStatus % (freq) Current smoker 32.2 (29) 37.1 (176) 0.271 4.2
Ex-smoker 52.2 (47) 45.9 (218)
Never smoked 14.4 (13) 12.2 ( 58)
<NA> 1.1 ( 1) 4.8 ( 23)
AlcoholUse % (freq) No 42.2 (38) 36.2 (172) 0.447 3.7
Yes 55.6 (50) 59.8 (284)
<NA> 2.2 ( 2) 4.0 ( 19)
DiabetesStatus % (freq) Control (no Diabetes Dx/Med) 77.8 (70) 79.2 (376) 0.878 0.0
Diabetes 22.2 (20) 20.8 ( 99)
Hypertension.selfreport % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.4
no 23.3 (21) 27.2 (129)
yes 76.7 (69) 72.4 (344)
<NA> 0.0 ( 0) 0.4 ( 2)
Hypertension.selfreportdrug % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.2
no 33.3 (30) 34.1 (162)
yes 66.7 (60) 65.7 (312)
<NA> 0.0 ( 0) 0.2 ( 1)
Hypertension.composite % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
no 8.9 ( 8) 13.7 ( 65)
yes 91.1 (82) 86.3 (410)
Hypertension.drugs % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.2
no 14.4 (13) 21.9 (104)
yes 84.4 (76) 78.1 (371)
<NA> 1.1 ( 1) 0.0 ( 0)
Med.anticoagulants % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.2
no 88.9 (80) 86.3 (410)
yes 10.0 ( 9) 13.7 ( 65)
<NA> 1.1 ( 1) 0.0 ( 0)
Med.all.antiplatelet % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.2
no 6.7 ( 6) 10.5 ( 50)
yes 92.2 (83) 89.5 (425)
<NA> 1.1 ( 1) 0.0 ( 0)
Med.Statin.LLD % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.2
no 20.0 (18) 28.6 (136)
yes 78.9 (71) 71.4 (339)
<NA> 1.1 ( 1) 0.0 ( 0)
Stroke_Dx % (freq) Missing 0.0 ( 0) 0.0 ( 0) NaN 6.0
No stroke diagnosed 83.3 (75) 75.2 (357)
Stroke diagnosed 10.0 ( 9) 18.9 ( 90)
<NA> 6.7 ( 6) 5.9 ( 28)
sympt % (freq) missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
Asymptomatic 100.0 (90) 0.0 ( 0)
TIA 0.0 ( 0) 48.4 (230)
minor stroke 0.0 ( 0) 20.8 ( 99)
Major stroke 0.0 ( 0) 8.2 ( 39)
Amaurosis fugax 0.0 ( 0) 15.8 ( 75)
Four vessel disease 0.0 ( 0) 2.7 ( 13)
Vertebrobasilary TIA 0.0 ( 0) 0.2 ( 1)
Retinal infarction 0.0 ( 0) 0.4 ( 2)
Symptomatic, but aspecific symtoms 0.0 ( 0) 3.4 ( 16)
Contralateral symptomatic occlusion 0.0 ( 0) 0.0 ( 0)
retinal infarction 0.0 ( 0) 0.0 ( 0)
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.0 ( 0) 0.0 ( 0)
retinal infarction + TIAs 0.0 ( 0) 0.0 ( 0)
Ocular ischemic syndrome 0.0 ( 0) 0.0 ( 0)
ischemisch glaucoom 0.0 ( 0) 0.0 ( 0)
subclavian steal syndrome 0.0 ( 0) 0.0 ( 0)
TGA 0.0 ( 0) 0.0 ( 0)
Symptoms.5G % (freq) Asymptomatic 100.0 (90) 0.0 ( 0) <0.001 0.0
Ocular 0.0 ( 0) 15.8 ( 75)
Other 0.0 ( 0) 6.1 ( 29)
Retinal infarction 0.0 ( 0) 0.4 ( 2)
Stroke 0.0 ( 0) 29.1 (138)
TIA 0.0 ( 0) 48.6 (231)
AsymptSympt % (freq) Asymptomatic 100.0 (90) 0.0 ( 0) <0.001 0.0
Ocular and others 0.0 ( 0) 22.3 (106)
Symptomatic 0.0 ( 0) 77.7 (369)
AsymptSympt2G % (freq) Asymptomatic 100.0 (90) 0.0 ( 0) <0.001 0.0
Symptomatic 0.0 ( 0) 100.0 (475)
restenos % (freq) missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
de novo 94.4 (85) 97.5 (463)
restenosis 5.6 ( 5) 2.5 ( 12)
stenose bij angioseal na PTCA 0.0 ( 0) 0.0 ( 0)
stenose % (freq) missing 0.0 ( 0) 0.0 ( 0) NaN 0.2
0-49% 0.0 ( 0) 0.6 ( 3)
50-70% 1.1 ( 1) 3.6 ( 17)
70-90% 45.6 (41) 39.2 (186)
90-99% 53.3 (48) 55.4 (263)
100% (Occlusion) 0.0 ( 0) 1.1 ( 5)
NA 0.0 ( 0) 0.0 ( 0)
50-99% 0.0 ( 0) 0.0 ( 0)
70-99% 0.0 ( 0) 0.0 ( 0)
99 0.0 ( 0) 0.0 ( 0)
<NA> 0.0 ( 0) 0.2 ( 1)
MedHx_CVD % (freq) No 38.9 (35) 36.2 (172) 0.716 0.0
yes 61.1 (55) 63.8 (303)
CAD_history % (freq) Missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
No history CAD 57.8 (52) 69.7 (331)
History CAD 42.2 (38) 30.3 (144)
PAOD % (freq) missing/no data 0.0 ( 0) 0.0 ( 0) NaN 0.0
no 71.1 (64) 79.4 (377)
yes 28.9 (26) 20.6 ( 98)
Peripheral.interv % (freq) no 66.7 (60) 85.5 (406) <0.001 0.0
yes 33.3 (30) 14.5 ( 69)
EP_composite % (freq) No data available. 0.0 ( 0) 0.0 ( 0) NaN 1.1
No composite endpoints 67.8 (61) 71.8 (341)
Composite endpoints 32.2 (29) 26.9 (128)
<NA> 0.0 ( 0) 1.3 ( 6)
EP_composite_time (mean (SD)) 2.515 (0.963) 2.640 (1.182) 0.345 1.1
macmean0 (mean (SD)) 0.886 (1.216) 0.969 (1.424) 0.606 0.2
smcmean0 (mean (SD)) 2.813 (3.017) 2.097 (2.354) 0.012 0.9
Macrophages.bin % (freq) no/minor 50.0 (45) 39.8 (189) 0.149 0.7
moderate/heavy 50.0 (45) 59.4 (282)
<NA> 0.0 ( 0) 0.8 ( 4)
SMC.bin % (freq) no/minor 17.8 (16) 30.9 (147) 0.032 0.5
moderate/heavy 81.1 (73) 68.6 (326)
<NA> 1.1 ( 1) 0.4 ( 2)
neutrophils (mean (SD)) 176.480 (534.398) 96.554 (135.533) 0.179 77.7
Mast_cells_plaque (mean (SD)) 124.969 (143.475) 164.421 (159.927) 0.206 72.0
IPH.bin % (freq) no 30.0 (27) 23.2 (110) 0.210 0.0
yes 70.0 (63) 76.8 (365)
vessel_density_averaged (mean (SD)) 8.580 (5.446) 9.046 (5.766) 0.488 2.1
Calc.bin % (freq) no/minor 27.8 (25) 43.2 (205) 0.009 0.0
moderate/heavy 72.2 (65) 56.8 (270)
Collagen.bin % (freq) no/minor 14.4 (13) 19.8 ( 94) 0.217 0.4
[ reached getOption("max.print") -- omitted 14 rows ]AEDB.CEA.subset.both <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015) & !is.na(MCP1))
AEDB.CEA.subset.both.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
strata = "AsymptSympt2G",
data = AEDB.CEA.subset.both, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:6] Stratified by AsymptSympt2G
level Asymptomatic Symptomatic p test Missing
n 60 375
Hospital % (freq) St. Antonius, Nieuwegein 66.7 (40) 68.3 (256) 0.922 0.0
UMC Utrecht 33.3 (20) 31.7 (119)
ORyear % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
2002 21.7 (13) 9.9 ( 37)
2003 16.7 (10) 25.1 ( 94)
2004 35.0 (21) 32.3 (121)
2005 21.7 (13) 28.5 (107)
2006 5.0 ( 3) 4.3 ( 16)
2007 0.0 ( 0) 0.0 ( 0)
2008 0.0 ( 0) 0.0 ( 0)
2009 0.0 ( 0) 0.0 ( 0)
2010 0.0 ( 0) 0.0 ( 0)
2011 0.0 ( 0) 0.0 ( 0)
2012 0.0 ( 0) 0.0 ( 0)
2013 0.0 ( 0) 0.0 ( 0)
2014 0.0 ( 0) 0.0 ( 0)
2015 0.0 ( 0) 0.0 ( 0)
2016 0.0 ( 0) 0.0 ( 0)
2017 0.0 ( 0) 0.0 ( 0)
2018 0.0 ( 0) 0.0 ( 0)
2019 0.0 ( 0) 0.0 ( 0)
Age (mean (SD)) 67.117 (8.267) 67.776 (8.838) 0.589 0.0
Gender % (freq) female 30.0 (18) 31.5 (118) 0.938 0.0
male 70.0 (42) 68.5 (257)
TC_finalCU (mean (SD)) 183.717 (46.043) 192.935 (48.831) 0.217 16.8
LDL_finalCU (mean (SD)) 105.471 (33.898) 115.117 (42.467) 0.141 26.4
HDL_finalCU (mean (SD)) 44.610 (15.458) 46.322 (15.468) 0.478 22.1
TG_finalCU (mean (SD)) 167.671 (96.628) 158.768 (84.857) 0.509 20.7
TC_final (mean (SD)) 4.758 (1.193) 4.997 (1.265) 0.217 16.8
LDL_final (mean (SD)) 2.732 (0.878) 2.982 (1.100) 0.141 26.4
HDL_final (mean (SD)) 1.155 (0.400) 1.200 (0.401) 0.478 22.1
TG_final (mean (SD)) 1.895 (1.092) 1.794 (0.959) 0.509 20.7
hsCRP_plasma (mean (SD)) 9.297 (27.925) 18.476 (97.053) 0.554 41.6
systolic (mean (SD)) 158.714 (23.698) 158.067 (25.904) 0.861 5.1
diastoli (mean (SD)) 82.768 (10.280) 84.045 (12.769) 0.476 5.1
GFR_MDRD (mean (SD)) 68.393 (19.904) 70.587 (18.444) 0.402 1.1
BMI (mean (SD)) 26.609 (3.071) 26.449 (3.909) 0.764 6.7
KDOQI % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 1.4
Normal kidney function 15.0 ( 9) 14.1 ( 53)
CKD 2 (Mild) 50.0 (30) 57.9 (217)
CKD 3 (Moderate) 31.7 (19) 25.6 ( 96)
CKD 4 (Severe) 0.0 ( 0) 0.8 ( 3)
CKD 5 (Failure) 1.7 ( 1) 0.3 ( 1)
<NA> 1.7 ( 1) 1.3 ( 5)
BMI_WHO % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 6.9
Underweight 0.0 ( 0) 1.6 ( 6)
Normal 28.3 (17) 33.3 (125)
Overweight 56.7 (34) 44.8 (168)
Obese 11.7 ( 7) 12.8 ( 48)
<NA> 3.3 ( 2) 7.5 ( 28)
SmokerStatus % (freq) Current smoker 36.7 (22) 37.9 (142) 0.398 3.7
Ex-smoker 51.7 (31) 45.9 (172)
Never smoked 11.7 ( 7) 12.0 ( 45)
<NA> 0.0 ( 0) 4.3 ( 16)
AlcoholUse % (freq) No 43.3 (26) 36.0 (135) 0.543 4.1
Yes 53.3 (32) 59.7 (224)
<NA> 3.3 ( 2) 4.3 ( 16)
DiabetesStatus % (freq) Control (no Diabetes Dx/Med) 73.3 (44) 80.0 (300) 0.314 0.0
Diabetes 26.7 (16) 20.0 ( 75)
Hypertension.selfreport % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.2
no 18.3 (11) 27.5 (103)
yes 81.7 (49) 72.3 (271)
<NA> 0.0 ( 0) 0.3 ( 1)
Hypertension.selfreportdrug % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.2
no 30.0 (18) 34.4 (129)
yes 70.0 (42) 65.3 (245)
<NA> 0.0 ( 0) 0.3 ( 1)
Hypertension.composite % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
no 5.0 ( 3) 14.1 ( 53)
yes 95.0 (57) 85.9 (322)
Hypertension.drugs % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
no 11.7 ( 7) 23.2 ( 87)
yes 88.3 (53) 76.8 (288)
Med.anticoagulants % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
no 88.3 (53) 85.9 (322)
yes 11.7 ( 7) 14.1 ( 53)
Med.all.antiplatelet % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
no 6.7 ( 4) 10.9 ( 41)
yes 93.3 (56) 89.1 (334)
Med.Statin.LLD % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
no 16.7 (10) 28.8 (108)
yes 83.3 (50) 71.2 (267)
Stroke_Dx % (freq) Missing 0.0 ( 0) 0.0 ( 0) NaN 5.7
No stroke diagnosed 85.0 (51) 74.1 (278)
Stroke diagnosed 10.0 ( 6) 20.0 ( 75)
<NA> 5.0 ( 3) 5.9 ( 22)
sympt % (freq) missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
Asymptomatic 100.0 (60) 0.0 ( 0)
TIA 0.0 ( 0) 48.3 (181)
minor stroke 0.0 ( 0) 20.5 ( 77)
Major stroke 0.0 ( 0) 9.1 ( 34)
Amaurosis fugax 0.0 ( 0) 16.3 ( 61)
Four vessel disease 0.0 ( 0) 2.9 ( 11)
Vertebrobasilary TIA 0.0 ( 0) 0.3 ( 1)
Retinal infarction 0.0 ( 0) 0.3 ( 1)
Symptomatic, but aspecific symtoms 0.0 ( 0) 2.4 ( 9)
Contralateral symptomatic occlusion 0.0 ( 0) 0.0 ( 0)
retinal infarction 0.0 ( 0) 0.0 ( 0)
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.0 ( 0) 0.0 ( 0)
retinal infarction + TIAs 0.0 ( 0) 0.0 ( 0)
Ocular ischemic syndrome 0.0 ( 0) 0.0 ( 0)
ischemisch glaucoom 0.0 ( 0) 0.0 ( 0)
subclavian steal syndrome 0.0 ( 0) 0.0 ( 0)
TGA 0.0 ( 0) 0.0 ( 0)
Symptoms.5G % (freq) Asymptomatic 100.0 (60) 0.0 ( 0) <0.001 0.0
Ocular 0.0 ( 0) 16.3 ( 61)
Other 0.0 ( 0) 5.3 ( 20)
Retinal infarction 0.0 ( 0) 0.3 ( 1)
Stroke 0.0 ( 0) 29.6 (111)
TIA 0.0 ( 0) 48.5 (182)
AsymptSympt % (freq) Asymptomatic 100.0 (60) 0.0 ( 0) <0.001 0.0
Ocular and others 0.0 ( 0) 21.9 ( 82)
Symptomatic 0.0 ( 0) 78.1 (293)
AsymptSympt2G % (freq) Asymptomatic 100.0 (60) 0.0 ( 0) <0.001 0.0
Symptomatic 0.0 ( 0) 100.0 (375)
restenos % (freq) missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
de novo 96.7 (58) 97.3 (365)
restenosis 3.3 ( 2) 2.7 ( 10)
stenose bij angioseal na PTCA 0.0 ( 0) 0.0 ( 0)
stenose % (freq) missing 0.0 ( 0) 0.0 ( 0) NaN 0.2
0-49% 0.0 ( 0) 0.5 ( 2)
50-70% 1.7 ( 1) 3.5 ( 13)
70-90% 43.3 (26) 37.6 (141)
90-99% 55.0 (33) 57.1 (214)
100% (Occlusion) 0.0 ( 0) 1.1 ( 4)
NA 0.0 ( 0) 0.0 ( 0)
50-99% 0.0 ( 0) 0.0 ( 0)
70-99% 0.0 ( 0) 0.0 ( 0)
99 0.0 ( 0) 0.0 ( 0)
<NA> 0.0 ( 0) 0.3 ( 1)
MedHx_CVD % (freq) No 43.3 (26) 36.5 (137) 0.386 0.0
yes 56.7 (34) 63.5 (238)
CAD_history % (freq) Missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
No history CAD 63.3 (38) 72.0 (270)
History CAD 36.7 (22) 28.0 (105)
PAOD % (freq) missing/no data 0.0 ( 0) 0.0 ( 0) NaN 0.0
no 70.0 (42) 78.4 (294)
yes 30.0 (18) 21.6 ( 81)
Peripheral.interv % (freq) no 66.7 (40) 84.8 (318) 0.001 0.0
yes 33.3 (20) 15.2 ( 57)
EP_composite % (freq) No data available. 0.0 ( 0) 0.0 ( 0) NaN 0.9
No composite endpoints 65.0 (39) 72.5 (272)
Composite endpoints 35.0 (21) 26.4 ( 99)
<NA> 0.0 ( 0) 1.1 ( 4)
EP_composite_time (mean (SD)) 2.558 (0.901) 2.654 (1.097) 0.525 0.9
macmean0 (mean (SD)) 1.002 (1.298) 0.897 (1.361) 0.579 0.0
smcmean0 (mean (SD)) 2.360 (1.885) 2.090 (2.356) 0.398 0.9
Macrophages.bin % (freq) no/minor 46.7 (28) 42.9 (161) 0.696 0.7
moderate/heavy 53.3 (32) 56.3 (211)
<NA> 0.0 ( 0) 0.8 ( 3)
SMC.bin % (freq) no/minor 18.3 (11) 29.6 (111) 0.132 0.7
moderate/heavy 80.0 (48) 69.9 (262)
<NA> 1.7 ( 1) 0.5 ( 2)
neutrophils (mean (SD)) 250.333 (686.374) 111.514 (148.306) 0.114 79.5
Mast_cells_plaque (mean (SD)) 111.524 (121.136) 176.736 (176.383) 0.112 75.2
IPH.bin % (freq) no 30.0 (18) 24.3 ( 91) 0.429 0.0
yes 70.0 (42) 75.7 (284)
vessel_density_averaged (mean (SD)) 7.956 (3.932) 9.124 (5.813) 0.147 2.3
Calc.bin % (freq) no/minor 25.0 (15) 42.1 (158) 0.018 0.0
moderate/heavy 75.0 (45) 57.9 (217)
Collagen.bin % (freq) no/minor 15.0 ( 9) 20.3 ( 76) 0.435 0.0
moderate/heavy 85.0 (51) 79.7 (299)
Fat.bin_10 % (freq) <10% 23.3 (14) 16.5 ( 62) 0.269 0.0
>10% 76.7 (46) 83.5 (313)
Fat.bin_40 % (freq) <40% 73.3 (44) 64.0 (240) 0.206 0.0
[ reached getOption("max.print") -- omitted 9 rows ]Writing the baseline table to Excel format.
# Write basetable
require(openxlsx)
write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.xlsx"),
AEDB.CEA.tableOne,
row.names = TRUE,
col.names = TRUE,
sheetName = "wholeAEDB_Baseline")
write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.AsymptSympt.xlsx"),
AEDB.CEA.subset.AsymptSympt.tableOne,
row.names = TRUE,
col.names = TRUE,
sheetName = "wholeAEDB_Baseline_Sympt")
write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEA.xlsx"),
AEDB.CEA.subset.combo.tableOne,
row.names = TRUE,
col.names = TRUE,
sheetName = "subsetAEDB_Baseline")
write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEAserum.AsymptSympt.xlsx"),
AEDB.CEA.subset.serum.tableOne,
row.names = TRUE,
col.names = TRUE,
sheetName = "subsetAEDB_Baseline_serum_Sympt")
Data exploration
Here we inspect the data and when necessary transform quantitative measures. We will inspect the raw, natural log transformed + the smallest measurement, and inverse-normal transformation.
summary(AEDB.CEA$MCP1_pg_ug_2015) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0005 0.1373 0.3401 0.6121 0.7232 10.8540 1225 do.call(rbind , by(AEDB.CEA$MCP1_pg_ug_2015, AEDB.CEA$AsymptSympt2G, summary)) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
Asymptomatic 0.0061401090 0.09779678 0.2148984 0.4950529 0.4982360 5.761795 139
Symptomatic 0.0004584575 0.14408036 0.3510514 0.6264834 0.7412862 10.853968 1086library(patchwork)
p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
# add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 plaque levels",
xlab = "pg/ug",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.min_MCP1_pg_ug_2015 <- min(AEDB.CEA$MCP1_pg_ug_2015, na.rm = TRUE)
min_MCP1_pg_ug_2015[1] 0.0004584575AEDB.CEA$MCP1_pg_ug_2015_LN <- log(AEDB.CEA$MCP1_pg_ug_2015 + min_MCP1_pg_ug_2015)
p2 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
# add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
# title = "MCP1 plaque levels",
xlab = "natural log-transformed pg/ug",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.AEDB.CEA$MCP1_pg_ug_2015_rank <- qnorm((rank(AEDB.CEA$MCP1_pg_ug_2015, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1_pg_ug_2015)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 plaque levels",
xlab = "inverse-normal transformation pg/ug",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.p1 
p2 
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "") | ggpar(p3, legend = "right")
rm(p1, p2, p3)
summary(AEDB.CEA$MCP1) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 56.42 103.00 135.76 178.99 926.27 1856 do.call(rbind , by(AEDB.CEA$MCP1, AEDB.CEA$AsymptSympt2G, summary)) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
Asymptomatic 0 39.39316 77.03422 114.1772 125.7124 846.5306 180
Symptomatic 0 59.35405 110.97343 139.8528 185.3418 926.2729 1676do.call(rbind , by(AEDB.CEA.subset.serum$MCP1_pg_ug_2015, AEDB.CEA.subset.serum$AsymptSympt2G, summary)) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
Asymptomatic 0.0110497585 0.09140971 0.1548690 0.4484342 0.3409975 3.967078 30
Symptomatic 0.0004584575 0.08262055 0.2041551 0.5773584 0.4433739 8.264774 100do.call(rbind , by(AEDB.CEA.subset.serum$MCP1, AEDB.CEA.subset.serum$AsymptSympt2G, summary)) Min. 1st Qu. Median Mean 3rd Qu. Max.
Asymptomatic 0 39.39316 77.03422 114.1772 125.7124 846.5306
Symptomatic 0 59.35405 110.97343 139.8528 185.3418 926.2729p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
# add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 serum levels",
xlab = "pg/mL",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.min_MCP1 <- min(AEDB.CEA$MCP1, na.rm = TRUE)
min_MCP1[1] 0AEDB.CEA$MCP1_LN <- log(AEDB.CEA$MCP1 + min_MCP1)
p2 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
# add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 serum levels",
xlab = "natural log-transformed pg/ug",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.AEDB.CEA$MCP1_rank <- qnorm((rank(AEDB.CEA$MCP1, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 serum levels",
xlab = "inverse-normal transformation pg/ug",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.p1 
p2 
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "") | ggpar(p3, legend = "right")
rm(p1, p2, p3)
Preliminary conclusion data exploration
In line with the previous work by Marios Georgakis we will apply natural log transformation on all proteins and focus the analysis on MCP1 in serum and plaque.
Analyses
The analyses are focused on three elements:
- plaque vulnerability phenotypes
- clinical status at inclusion (symptoms)
- secondary clinical outcome during three (3) years of follow-up
Covariates & other variables
- Age (continuous in 1-year increment). [
Age] - Sex (male vs. female). [
Gender] - Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [
Hypertension.composite] - Presence of diabetes mellitus at baseline (defined either as a history of diabetes and/or administration of glucose lowering medication). [
DiabetesStatus] - Smoking (current, ex-, never). [
SmokerStatus] - LDL-C levels (continuous). [
LDL_final] - Use of lipid-lowering drugs. [
Med.Statin.LLD] - Use of antiplatelet drugs. [
Med.all.antiplatelet] - eGFR (continuous). [
GFR_MDRD] - BMI (continuous). [
BMI] - History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [
MedHx_CVD] combination of [CAD_history,Stroke_history,Peripheral.interv] - Level of stenosis (50-70% vs. 70-99%). [
stenose]
Models
We will analyze the data through four different models
- Model 1: adjusted for age and sex
- Model 2: adjusted for age, sex, and additionally adjusted for history hypertension (defined from medical history and/or use of antihypertensive medications), diabetes (defined as history of a diagnosis and/or use of glucose-lowering medications), current smoking, LDL-C levels at time of operation, use of statins, use of antiplatelet agents, eGFR, BMI, history of cardiovascular disease (coronary artery disease, stroke, peripheral artery disease), and level of stenosis (50-70%, 70-90%, 90-99%)
A. Cross-sectional analysis plaque phenotypes
In the cross-sectional analysis of plaque and serum MCP1, IL6, and IL6R levels we will focus on the following plaque vulnerability phenotypes:
- Percentage of macrophages (continuous trait)
- Percentage of SMCs (continuous trait)
- Number of intraplaque microvessels per 3-4 hotspots (continuous trait)
- Presence of moderate/heavy calcifications (binary trait)
- Presence of moderate/heavy collagen content (binary trait)
- Presence of lipid core no/<10% vs. >10% (binary trait)
- Presence of intraplaque hemorrhage (binary trait)
Continous traits
# macrophages
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$macmean0) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0000 0.0733 0.3133 0.7676 1.0000 15.1000 720 min_macmean <- min(AEDB.CEA$macmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % macrophages: ",min_macmean,".\n"))
Minimum value % macrophages: 0.AEDB.CEA$Macrophages_LN <- log(AEDB.CEA$macmean0 + min_macmean)
ggpubr::gghistogram(AEDB.CEA, "Macrophages_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "% macrophages",
xlab = "natural log-transformed %",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
AEDB.CEA$Macrophages_rank <- qnorm((rank(AEDB.CEA$macmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$macmean0)))
ggpubr::gghistogram(AEDB.CEA, "Macrophages_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "% macrophages",
xlab = "inverse-rank normalized %",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
# smooth muscle cells
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$macmean0) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0000 0.0733 0.3133 0.7676 1.0000 15.1000 720 min_smcmean <- min(AEDB.CEA$smcmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % smooth muscle cells: ",min_smcmean,".\n"))
Minimum value % smooth muscle cells: 0.AEDB.CEA$SMC_LN <- log(AEDB.CEA$smcmean0 + min_smcmean)
ggpubr::gghistogram(AEDB.CEA, "SMC_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "% smooth muscle cells",
xlab = "natural log-transformed %",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
AEDB.CEA$SMC_rank <- qnorm((rank(AEDB.CEA$smcmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$smcmean0)))
ggpubr::gghistogram(AEDB.CEA, "SMC_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "% smooth muscle cells",
xlab = "inverse-rank normalized %",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
# vessel density
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$vessel_density_averaged) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 4.000 7.000 8.318 11.300 48.000 850 min_vesseldensity <- min(AEDB.CEA$vessel_density_averaged, na.rm = TRUE)
min_vesseldensity[1] 0cat(paste0("\nMinimum value number of intraplaque neovessels per 3-4 hotspots: ",min_vesseldensity,".\n"))
Minimum value number of intraplaque neovessels per 3-4 hotspots: 0.AEDB.CEA$VesselDensity_LN <- log(AEDB.CEA$vessel_density_averaged + min_vesseldensity)
ggpubr::gghistogram(AEDB.CEA, "VesselDensity_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "number of intraplaque neovessels per 3-4 hotspots",
xlab = "natural log-transformed number",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
AEDB.CEA$VesselDensity_rank <- qnorm((rank(AEDB.CEA$vessel_density_averaged, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$vessel_density_averaged)))
ggpubr::gghistogram(AEDB.CEA, "VesselDensity_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "number of intraplaque neovessels per 3-4 hotspots",
xlab = "inverse-rank normalized number",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
Binary traits
# calcification
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$Calc.bin) no/minor moderate/heavy NA's
1006 849 566 contrasts(AEDB.CEA$Calc.bin) moderate/heavy
no/minor 0
moderate/heavy 1AEDB.CEA$CalcificationPlaque <- as.factor(AEDB.CEA$Calc.bin)
df <- AEDB.CEA %>%
filter(!is.na(CalcificationPlaque)) %>%
group_by(Gender, CalcificationPlaque) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "CalcificationPlaque", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Calcification",
xlab = "calcification",
ggtheme = theme_minimal())
rm(df)
# collagen
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$Collagen.bin) no/minor moderate/heavy NA's
382 1467 572 contrasts(AEDB.CEA$Collagen.bin) moderate/heavy
no/minor 0
moderate/heavy 1AEDB.CEA$CollagenPlaque <- as.factor(AEDB.CEA$Collagen.bin)
df <- AEDB.CEA %>%
filter(!is.na(CollagenPlaque)) %>%
group_by(Gender, CollagenPlaque) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "CollagenPlaque", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Collagen",
xlab = "collagen",
ggtheme = theme_minimal())
rm(df)
# fat 10%
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$Fat.bin_10) <10% >10% NA's
542 1314 565 contrasts(AEDB.CEA$Fat.bin_10) >10%
<10% 0
>10% 1AEDB.CEA$Fat10Perc <- as.factor(AEDB.CEA$Fat.bin_10)
df <- AEDB.CEA %>%
filter(!is.na(Fat10Perc)) %>%
group_by(Gender, Fat10Perc) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "Fat10Perc", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Intraplaque fat",
xlab = "intraplaque fat",
ggtheme = theme_minimal())
rm(df)
# IPH
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$IPH.bin) no yes NA's
744 1108 569 contrasts(AEDB.CEA$IPH.bin) yes
no 0
yes 1AEDB.CEA$IPH <- as.factor(AEDB.CEA$IPH.bin)
df <- AEDB.CEA %>%
filter(!is.na(IPH)) %>%
group_by(Gender, IPH) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "IPH", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Intraplaque hemorrhage",
xlab = "intraplaque hemorrhage",
ggtheme = theme_minimal())
rm(df)
# Symptoms
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$AsymptSympt) Asymptomatic Ocular and others Symptomatic
270 540 1611 contrasts(AEDB.CEA$AsymptSympt) Ocular and others Symptomatic
Asymptomatic 0 0
Ocular and others 1 0
Symptomatic 0 1AEDB.CEA$AsymptSympt <- as.factor(AEDB.CEA$AsymptSympt)
df <- AEDB.CEA %>%
filter(!is.na(AsymptSympt)) %>%
group_by(Gender, AsymptSympt) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "AsymptSympt", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Symptoms",
xlab = "symptoms",
ggtheme = theme_minimal())
rm(df)
In this section we make some variables to assist with analysis.
AEDB.CEA.samplesize = nrow(AEDB.CEA)
# TRAITS.PROTEIN = c("IL6_LN", "MCP1_LN", "IL6_pg_ug_2015_LN", "IL6R_pg_ug_2015_LN", "MCP1_pg_ug_2015_LN")
# TRAITS.PROTEIN.RANK = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank")
TRAITS.PROTEIN.RANK = c("MCP1_pg_ug_2015_rank", "MCP1_rank")
# TRAITS.CON = c("Macrophages_LN", "SMC_LN", "VesselDensity_LN")
TRAITS.CON.RANK = c("Macrophages_rank", "SMC_rank", "VesselDensity_rank")
TRAITS.BIN = c("CalcificationPlaque", "CollagenPlaque", "Fat10Perc", "IPH")
# "Hospital",
# "Age", "Gender",
# "TC_final", "LDL_final", "HDL_final", "TG_final",
# "systolic", "diastoli", "GFR_MDRD", "BMI",
# "KDOQI", "BMI_WHO",
# "SmokerCurrent", "eCigarettes", "ePackYearsSmoking",
# "DiabetesStatus", "Hypertension.composite",
# "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
# "Stroke_Dx", "sympt", "Symptoms.5G", "restenos",
# "EP_composite", "EP_composite_time",
# "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
# "neutrophils", "Mast_cells_plaque",
# "IPH.bin", "vessel_density_averaged",
# "Calc.bin", "Collagen.bin",
# "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
# "IL6_pg_ug_2015", "MCP1_pg_ug_2015",
# "QC2018_FILTER", "CHIP", "SAMPLE_TYPE",
# "CAD_history", "Stroke_history", "Peripheral.interv",
# "stenose"
# 1. Age (continuous in 1-year increment). [Age]
# 2. Sex (male vs. female). [Gender]
# 3. Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
# 4. Presence of diabetes mellitus at baseline (defined either as a history of diabetes, administration of glucose lowering medication, HbA1c ≥6.5%, fasting glucose ≥126 mg/dl, .or random glucose levels ≥200 mg/dl). [DiabetesStatus]
# 5. Smoking (current, ex-, never). [SmokerCurrent]
# 6. LDL-C levels (continuous). [LDL_final]
# 7. Use of lipid-lowering drugs. [Med.Statin.LLD]
# 8. Use of antiplatelet drugs. [Med.all.antiplatelet]
# 9. eGFR (continuous). [GFR_MDRD]
# 10. BMI (continuous). [BMI]
# 11. History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [MedHx_CVD] combinatino of: [CAD_history, Stroke_history, Peripheral.interv]
# 12. Level of stenosis (50-70% vs. 70-99%). [stenose]
# Models
# Model 1: adjusted for age and sex
# Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,
COVARIATES_M1 = c("Age", "Gender")
COVARIATES_M2 = c(COVARIATES_M1,
"Hypertension.composite", "DiabetesStatus",
"SmokerStatus",
# "SmokerCurrent",
"Med.Statin.LLD", "Med.all.antiplatelet",
"GFR_MDRD", "BMI",
# "CAD_history", "Stroke_history", "Peripheral.interv",
"MedHx_CVD",
"stenose")
# COVARIATES_M3 = c(COVARIATES_M2, "LDL_final")
# COVARIATES_M4 = c(COVARIATES_M2, "hsCRP_plasma")
# COVARIATES_M5 = c(COVARIATES_M2, "IL6_pg_ug_2015_LN")
# COVARIATES_M5rank = c(COVARIATES_M2, "IL6_pg_ug_2015_rank")
Model 1
In this model we correct for Age and Gender.
Inverse-rank normalization
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Analysis of continuous/quantitative plaque traits as a function of serum/plaque MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")Running linear regression...for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.CON.RANK)) {
TRAIT = TRAITS.CON.RANK[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender, data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of MCP1_pg_ug_2015_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale
-0.08694 -0.04450 0.10580
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4082 -0.6842 0.0046 0.6589 3.3363
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0417401 0.2249607 -0.186 0.8528
currentDF[, TRAIT] -0.0450829 0.0294144 -1.533 0.1256
Age -0.0006612 0.0031991 -0.207 0.8363
Gendermale 0.1060371 0.0635635 1.668 0.0955 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9995 on 1164 degrees of freedom
Multiple R-squared: 0.004088, Adjusted R-squared: 0.001522
F-statistic: 1.593 on 3 and 1164 DF, p-value: 0.1894
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: -0.045083
Standard error............: 0.029414
Odds ratio (effect size)..: 0.956
Lower 95% CI..............: 0.902
Upper 95% CI..............: 1.013
T-value...................: -1.532681
P-value...................: 0.1256263
R^2.......................: 0.004088
Adjusted r^2..............: 0.001522
Sample size of AE DB......: 2421
Sample size of model......: 1168
Missing data %............: 51.75547
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
-0.01596 -0.11159
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4354 -0.6542 -0.0098 0.6350 3.3670
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.112040 0.228792 0.490 0.624438
currentDF[, TRAIT] -0.111735 0.030680 -3.642 0.000283 ***
Age -0.002497 0.003236 -0.772 0.440423
Gendermale 0.062484 0.063873 0.978 0.328152
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9952 on 1160 degrees of freedom
Multiple R-squared: 0.01319, Adjusted R-squared: 0.01064
F-statistic: 5.169 on 3 and 1160 DF, p-value: 0.001498
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.111735
Standard error............: 0.03068
Odds ratio (effect size)..: 0.894
Lower 95% CI..............: 0.842
Upper 95% CI..............: 0.95
T-value...................: -3.641935
P-value...................: 0.000282537
R^2.......................: 0.013191
Adjusted r^2..............: 0.010639
Sample size of AE DB......: 2421
Sample size of model......: 1164
Missing data %............: 51.92069
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale
-0.1146 -0.1291 0.1266
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2793 -0.6743 0.0015 0.6370 3.4081
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.142513 0.233373 -0.611 0.5415
currentDF[, TRAIT] -0.129023 0.030547 -4.224 2.6e-05 ***
Age 0.000407 0.003312 0.123 0.9022
Gendermale 0.126560 0.065772 1.924 0.0546 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.002 on 1088 degrees of freedom
Multiple R-squared: 0.01916, Adjusted R-squared: 0.01645
F-statistic: 7.084 on 3 and 1088 DF, p-value: 0.0001025
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.129023
Standard error............: 0.030547
Odds ratio (effect size)..: 0.879
Lower 95% CI..............: 0.828
Upper 95% CI..............: 0.933
T-value...................: -4.223732
P-value...................: 2.60385e-05
R^2.......................: 0.019159
Adjusted r^2..............: 0.016454
Sample size of AE DB......: 2421
Sample size of model......: 1092
Missing data %............: 54.89467
Analysis of MCP1_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale
0.33995 0.10722 -0.00823 0.28614
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.6647 -0.6698 -0.0055 0.6538 2.9704
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.339946 0.327467 1.038 0.29967
currentDF[, TRAIT] 0.107224 0.038482 2.786 0.00551 **
Age -0.008230 0.004734 -1.738 0.08270 .
Gendermale 0.286142 0.091290 3.134 0.00181 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.98 on 560 degrees of freedom
Multiple R-squared: 0.03665, Adjusted R-squared: 0.03149
F-statistic: 7.102 on 3 and 560 DF, p-value: 0.0001089
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0.107224
Standard error............: 0.038482
Odds ratio (effect size)..: 1.113
Lower 95% CI..............: 1.032
Upper 95% CI..............: 1.2
T-value...................: 2.786343
P-value...................: 0.00551135
R^2.......................: 0.036652
Adjusted r^2..............: 0.031492
Sample size of AE DB......: 2421
Sample size of model......: 564
Missing data %............: 76.70384
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale
0.74531 -0.18163 -0.01324 0.23452
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.67223 -0.64089 -0.03124 0.64134 2.69212
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.74530 0.33158 2.248 0.02498 *
currentDF[, TRAIT] -0.18163 0.03963 -4.584 5.65e-06 ***
Age -0.01324 0.00477 -2.776 0.00570 **
Gendermale 0.23452 0.09053 2.591 0.00983 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9621 on 556 degrees of freedom
Multiple R-squared: 0.05804, Adjusted R-squared: 0.05296
F-statistic: 11.42 on 3 and 556 DF, p-value: 2.817e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.181633
Standard error............: 0.039626
Odds ratio (effect size)..: 0.834
Lower 95% CI..............: 0.772
Upper 95% CI..............: 0.901
T-value...................: -4.583677
P-value...................: 5.646691e-06
R^2.......................: 0.058042
Adjusted r^2..............: 0.05296
Sample size of AE DB......: 2421
Sample size of model......: 560
Missing data %............: 76.86906
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale
0.39691 -0.09764 -0.00880 0.31635
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.69243 -0.65649 0.00037 0.65337 2.74009
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.39691 0.33186 1.196 0.23221
currentDF[, TRAIT] -0.09764 0.05111 -1.911 0.05658 .
Age -0.00880 0.00479 -1.837 0.06674 .
Gendermale 0.31635 0.09247 3.421 0.00067 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9842 on 549 degrees of freedom
Multiple R-squared: 0.03247, Adjusted R-squared: 0.02718
F-statistic: 6.142 on 3 and 549 DF, p-value: 0.0004118
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.097643
Standard error............: 0.051107
Odds ratio (effect size)..: 0.907
Lower 95% CI..............: 0.821
Upper 95% CI..............: 1.003
T-value...................: -1.910561
P-value...................: 0.05658168
R^2.......................: 0.032471
Adjusted r^2..............: 0.027184
Sample size of AE DB......: 2421
Sample size of model......: 553
Missing data %............: 77.1582 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Analysis of binary plaque traits as a function of serum/plaque MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.BIN)) {
TRAIT = TRAITS.BIN[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of MCP1_pg_ug_2015_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale
-0.88365 -0.44949 0.01414 -0.19877
Degrees of Freedom: 1177 Total (i.e. Null); 1174 Residual
Null Deviance: 1632
Residual Deviance: 1570 AIC: 1578
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7790 -1.1167 -0.7616 1.1294 1.9050
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.883647 0.460710 -1.918 0.0551 .
currentDF[, PROTEIN] -0.449493 0.062887 -7.148 8.83e-13 ***
Age 0.014139 0.006548 2.159 0.0308 *
Gendermale -0.198771 0.129909 -1.530 0.1260
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1632.5 on 1177 degrees of freedom
Residual deviance: 1570.1 on 1174 degrees of freedom
AIC: 1578.1
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.449493
Standard error............: 0.062887
Odds ratio (effect size)..: 0.638
Lower 95% CI..............: 0.564
Upper 95% CI..............: 0.722
Z-value...................: -7.147681
P-value...................: 8.825609e-13
Hosmer and Lemeshow r^2...: 0.038193
Cox and Snell r^2.........: 0.051552
Nagelkerke's pseudo r^2...: 0.068747
Sample size of AE DB......: 2421
Sample size of model......: 1178
Missing data %............: 51.34242
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN],
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN]
1.3310 -0.2324
Degrees of Freedom: 1178 Total (i.e. Null); 1177 Residual
Null Deviance: 1216
Residual Deviance: 1205 AIC: 1209
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1020 0.5729 0.6568 0.7158 0.9553
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.143689 0.546619 2.092 0.03641 *
currentDF[, PROTEIN] -0.231697 0.072337 -3.203 0.00136 **
Age 0.002892 0.007791 0.371 0.71047
Gendermale -0.015641 0.156179 -0.100 0.92023
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1215.6 on 1178 degrees of freedom
Residual deviance: 1205.0 on 1175 degrees of freedom
AIC: 1213
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.231697
Standard error............: 0.072337
Odds ratio (effect size)..: 0.793
Lower 95% CI..............: 0.688
Upper 95% CI..............: 0.914
Z-value...................: -3.202999
P-value...................: 0.001360044
Hosmer and Lemeshow r^2...: 0.008726
Cox and Snell r^2.........: 0.008956
Nagelkerke's pseudo r^2...: 0.013921
Sample size of AE DB......: 2421
Sample size of model......: 1179
Missing data %............: 51.30112
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale
-0.32933 0.15569 0.01093 0.85630
Degrees of Freedom: 1178 Total (i.e. Null); 1175 Residual
Null Deviance: 1386
Residual Deviance: 1339 AIC: 1347
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9399 -1.2794 0.6824 0.7695 1.2262
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.329327 0.501681 -0.656 0.5115
currentDF[, PROTEIN] 0.155685 0.066807 2.330 0.0198 *
Age 0.010928 0.007185 1.521 0.1283
Gendermale 0.856297 0.137117 6.245 4.24e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1386.5 on 1178 degrees of freedom
Residual deviance: 1338.8 on 1175 degrees of freedom
AIC: 1346.8
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.155685
Standard error............: 0.066807
Odds ratio (effect size)..: 1.168
Lower 95% CI..............: 1.025
Upper 95% CI..............: 1.332
Z-value...................: 2.330372
P-value...................: 0.01978647
Hosmer and Lemeshow r^2...: 0.034376
Cox and Snell r^2.........: 0.039619
Nagelkerke's pseudo r^2...: 0.057296
Sample size of AE DB......: 2421
Sample size of model......: 1179
Missing data %............: 51.30112
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale
0.02945 -0.12895 0.61756
Degrees of Freedom: 1175 Total (i.e. Null); 1173 Residual
Null Deviance: 1572
Residual Deviance: 1546 AIC: 1552
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.650 -1.271 0.884 0.956 1.342
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.157708 0.462022 -0.341 0.7328
currentDF[, PROTEIN] -0.128693 0.060765 -2.118 0.0342 *
Age 0.002737 0.006577 0.416 0.6773
Gendermale 0.616805 0.129037 4.780 1.75e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1572.3 on 1175 degrees of freedom
Residual deviance: 1545.6 on 1172 degrees of freedom
AIC: 1553.6
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IPH
Effect size...............: -0.128693
Standard error............: 0.060765
Odds ratio (effect size)..: 0.879
Lower 95% CI..............: 0.781
Upper 95% CI..............: 0.99
Z-value...................: -2.117883
P-value...................: 0.03418496
Hosmer and Lemeshow r^2...: 0.016998
Cox and Snell r^2.........: 0.022471
Nagelkerke's pseudo r^2...: 0.030474
Sample size of AE DB......: 2421
Sample size of model......: 1176
Missing data %............: 51.42503
Analysis of MCP1_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age
-0.66894 0.01552
Degrees of Freedom: 564 Total (i.e. Null); 563 Residual
Null Deviance: 763.6
Residual Deviance: 761.1 AIC: 765.1
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.5354 -1.3063 0.9435 1.0328 1.2735
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.520510 0.681656 -0.764 0.445
currentDF[, PROTEIN] -0.084572 0.087486 -0.967 0.334
Age 0.015036 0.009908 1.518 0.129
Gendermale -0.159870 0.193286 -0.827 0.408
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 763.63 on 564 degrees of freedom
Residual deviance: 759.27 on 561 degrees of freedom
AIC: 767.27
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.084572
Standard error............: 0.087486
Odds ratio (effect size)..: 0.919
Lower 95% CI..............: 0.774
Upper 95% CI..............: 1.091
Z-value...................: -0.966696
P-value...................: 0.3336962
Hosmer and Lemeshow r^2...: 0.005706
Cox and Snell r^2.........: 0.007683
Nagelkerke's pseudo r^2...: 0.010366
Sample size of AE DB......: 2421
Sample size of model......: 565
Missing data %............: 76.66254
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN],
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN]
1.5378 -0.5379
Degrees of Freedom: 562 Total (i.e. Null); 561 Residual
Null Deviance: 547.6
Residual Deviance: 524.3 AIC: 528.3
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.4625 0.4102 0.5643 0.6848 1.1638
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.84690 0.91211 3.121 0.0018 **
currentDF[, PROTEIN] -0.54841 0.11723 -4.678 2.9e-06 ***
Age -0.01803 0.01302 -1.384 0.1663
Gendermale -0.11699 0.25442 -0.460 0.6456
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 547.57 on 562 degrees of freedom
Residual deviance: 522.07 on 559 degrees of freedom
AIC: 530.07
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.548413
Standard error............: 0.117233
Odds ratio (effect size)..: 0.578
Lower 95% CI..............: 0.459
Upper 95% CI..............: 0.727
Z-value...................: -4.677968
P-value...................: 2.897316e-06
Hosmer and Lemeshow r^2...: 0.046582
Cox and Snell r^2.........: 0.044294
Nagelkerke's pseudo r^2...: 0.071224
Sample size of AE DB......: 2421
Sample size of model......: 563
Missing data %............: 76.74515
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale
1.1894 0.6807 0.5650
Degrees of Freedom: 564 Total (i.e. Null); 562 Residual
Null Deviance: 554.2
Residual Deviance: 509.3 AIC: 515.3
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.4929 0.3605 0.5225 0.6691 1.3725
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.85428 0.86465 0.988 0.3231
currentDF[, PROTEIN] 0.68303 0.12124 5.634 1.76e-08 ***
Age 0.00499 0.01257 0.397 0.6914
Gendermale 0.56378 0.23128 2.438 0.0148 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 554.19 on 564 degrees of freedom
Residual deviance: 509.11 on 561 degrees of freedom
AIC: 517.11
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.68303
Standard error............: 0.12124
Odds ratio (effect size)..: 1.98
Lower 95% CI..............: 1.561
Upper 95% CI..............: 2.511
Z-value...................: 5.633721
P-value...................: 1.763624e-08
Hosmer and Lemeshow r^2...: 0.081344
Cox and Snell r^2.........: 0.076687
Nagelkerke's pseudo r^2...: 0.122697
Sample size of AE DB......: 2421
Sample size of model......: 565
Missing data %............: 76.66254
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age Gendermale
-1.02619 0.02446 0.78389
Degrees of Freedom: 564 Total (i.e. Null); 562 Residual
Null Deviance: 625.9
Residual Deviance: 607.1 AIC: 613.1
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0414 0.5339 0.6471 0.7406 1.2555
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.06199 0.77564 -1.369 0.170946
currentDF[, PROTEIN] 0.10109 0.10144 0.997 0.318965
Age 0.02532 0.01139 2.224 0.026177 *
Gendermale 0.75630 0.20942 3.611 0.000304 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 625.93 on 564 degrees of freedom
Residual deviance: 606.14 on 561 degrees of freedom
AIC: 614.14
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IPH
Effect size...............: 0.101091
Standard error............: 0.101437
Odds ratio (effect size)..: 1.106
Lower 95% CI..............: 0.907
Upper 95% CI..............: 1.35
Z-value...................: 0.996588
P-value...................: 0.3189645
Hosmer and Lemeshow r^2...: 0.031618
Cox and Snell r^2.........: 0.034421
Nagelkerke's pseudo r^2...: 0.051395
Sample size of AE DB......: 2421
Sample size of model......: 565
Missing data %............: 76.66254 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Model 2
In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis.
Inverse-rank normalization
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Analysis of continuous/quantitative plaque traits as a function of serum/plaque MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")Running linear regression...for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.CON.RANK)) {
TRAIT = TRAITS.CON.RANK[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender +
Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose,
data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of MCP1_pg_ug_2015_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Hypertension.composite +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Hypertension.compositeyes Med.Statin.LLDyes
0.30069 -0.05373 -0.20206 -0.14604
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3053 -0.6548 -0.0242 0.6293 3.3436
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.2390578 0.6042876 0.396 0.6925
currentDF[, TRAIT] -0.0612856 0.0317785 -1.929 0.0541 .
Age -0.0022303 0.0039334 -0.567 0.5708
Gendermale 0.0974448 0.0709000 1.374 0.1696
Hypertension.compositeyes -0.2024463 0.0966092 -2.096 0.0364 *
DiabetesStatusDiabetes -0.0289768 0.0772772 -0.375 0.7078
SmokerStatusEx-smoker -0.0230909 0.0731098 -0.316 0.7522
SmokerStatusNever smoked 0.1337644 0.1030877 1.298 0.1947
Med.Statin.LLDyes -0.1502085 0.0774112 -1.940 0.0526 .
Med.all.antiplateletyes 0.0018191 0.1086828 0.017 0.9866
GFR_MDRD -0.0005437 0.0016784 -0.324 0.7461
BMI -0.0026857 0.0088250 -0.304 0.7609
MedHx_CVDyes 0.0199269 0.0664034 0.300 0.7642
stenose50-70% 0.2704089 0.4317030 0.626 0.5312
stenose70-90% 0.3081981 0.4140028 0.744 0.4568
stenose90-99% 0.1802418 0.4140082 0.435 0.6634
stenose100% (Occlusion) -0.2457719 0.5317403 -0.462 0.6440
stenose50-99% 0.4778476 0.6484366 0.737 0.4613
stenose70-99% 0.4432860 0.5811508 0.763 0.4458
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.002 on 1000 degrees of freedom
Multiple R-squared: 0.0227, Adjusted R-squared: 0.005108
F-statistic: 1.29 on 18 and 1000 DF, p-value: 0.1851
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: -0.061286
Standard error............: 0.031778
Odds ratio (effect size)..: 0.941
Lower 95% CI..............: 0.884
Upper 95% CI..............: 1.001
T-value...................: -1.928524
P-value...................: 0.05407278
R^2.......................: 0.0227
Adjusted r^2..............: 0.005108
Sample size of AE DB......: 2421
Sample size of model......: 1019
Missing data %............: 57.90995
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Hypertension.composite +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Hypertension.compositeyes Med.Statin.LLDyes
0.3084 -0.1064 -0.2034 -0.1530
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3130 -0.6745 -0.0099 0.6299 3.3696
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.3219731 0.6046171 0.533 0.59448
currentDF[, TRAIT] -0.1029968 0.0329730 -3.124 0.00184 **
Age -0.0034454 0.0039631 -0.869 0.38485
Gendermale 0.0487194 0.0717747 0.679 0.49743
Hypertension.compositeyes -0.1954166 0.0965630 -2.024 0.04327 *
DiabetesStatusDiabetes -0.0219028 0.0772121 -0.284 0.77672
SmokerStatusEx-smoker -0.0209122 0.0731694 -0.286 0.77509
SmokerStatusNever smoked 0.1054769 0.1030387 1.024 0.30624
Med.Statin.LLDyes -0.1609021 0.0774630 -2.077 0.03804 *
Med.all.antiplateletyes -0.0039324 0.1085928 -0.036 0.97112
GFR_MDRD -0.0004018 0.0016792 -0.239 0.81092
BMI -0.0026011 0.0088289 -0.295 0.76836
MedHx_CVDyes 0.0116355 0.0664377 0.175 0.86101
stenose50-70% 0.3063875 0.4313296 0.710 0.47766
stenose70-90% 0.3397682 0.4136695 0.821 0.41164
stenose90-99% 0.2223931 0.4138010 0.537 0.59108
stenose100% (Occlusion) -0.1771374 0.5313862 -0.333 0.73894
stenose50-99% 0.5447894 0.6479149 0.841 0.40064
stenose70-99% 0.4871557 0.5806444 0.839 0.40168
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.001 on 996 degrees of freedom
Multiple R-squared: 0.02833, Adjusted R-squared: 0.01077
F-statistic: 1.613 on 18 and 996 DF, p-value: 0.05023
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.102997
Standard error............: 0.032973
Odds ratio (effect size)..: 0.902
Lower 95% CI..............: 0.846
Upper 95% CI..............: 0.962
T-value...................: -3.123675
P-value...................: 0.00183768
R^2.......................: 0.028328
Adjusted r^2..............: 0.010768
Sample size of AE DB......: 2421
Sample size of model......: 1015
Missing data %............: 58.07518
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Hypertension.composite +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Hypertension.compositeyes Med.Statin.LLDyes
0.2912 -0.1439 -0.1849 -0.1662
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1614 -0.6484 -0.0233 0.6402 3.4332
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.1767459 0.6402229 0.276 0.7826
currentDF[, TRAIT] -0.1420732 0.0332558 -4.272 2.13e-05 ***
Age -0.0009175 0.0040589 -0.226 0.8212
Gendermale 0.1109606 0.0733445 1.513 0.1307
Hypertension.compositeyes -0.1942298 0.1002766 -1.937 0.0531 .
DiabetesStatusDiabetes -0.0328877 0.0819413 -0.401 0.6882
SmokerStatusEx-smoker -0.0313111 0.0760495 -0.412 0.6806
SmokerStatusNever smoked 0.1139310 0.1071394 1.063 0.2879
Med.Statin.LLDyes -0.1671740 0.0796915 -2.098 0.0362 *
Med.all.antiplateletyes 0.0397565 0.1144791 0.347 0.7285
GFR_MDRD -0.0010896 0.0017515 -0.622 0.5340
BMI -0.0016953 0.0091572 -0.185 0.8532
MedHx_CVDyes 0.0074801 0.0688297 0.109 0.9135
stenose50-70% 0.1841594 0.4727540 0.390 0.6970
stenose70-90% 0.2563949 0.4542951 0.564 0.5726
stenose90-99% 0.1436685 0.4538378 0.317 0.7516
stenose100% (Occlusion) -0.2562619 0.5637405 -0.455 0.6495
stenose50-99% 0.5911304 0.6754523 0.875 0.3817
stenose70-99% 0.1673943 0.6766921 0.247 0.8047
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.005 on 932 degrees of freedom
Multiple R-squared: 0.03977, Adjusted R-squared: 0.02122
F-statistic: 2.144 on 18 and 932 DF, p-value: 0.003707
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.142073
Standard error............: 0.033256
Odds ratio (effect size)..: 0.868
Lower 95% CI..............: 0.813
Upper 95% CI..............: 0.926
T-value...................: -4.272134
P-value...................: 2.134847e-05
R^2.......................: 0.039768
Adjusted r^2..............: 0.021223
Sample size of AE DB......: 2421
Sample size of model......: 951
Missing data %............: 60.71871
Analysis of MCP1_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + Med.Statin.LLD +
Med.all.antiplatelet, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale Hypertension.compositeyes DiabetesStatusDiabetes
0.40394 0.10151 -0.00769 0.29093 -0.20416 -0.16323
Med.Statin.LLDyes Med.all.antiplateletyes
-0.20536 0.30544
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.11250 -0.68313 0.01191 0.63865 2.77619
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.998499 0.844401 1.182 0.23759
currentDF[, TRAIT] 0.095108 0.041424 2.296 0.02210 *
Age -0.010746 0.005714 -1.880 0.06064 .
Gendermale 0.305016 0.100158 3.045 0.00245 **
Hypertension.compositeyes -0.233380 0.133570 -1.747 0.08122 .
DiabetesStatusDiabetes -0.155787 0.112122 -1.389 0.16534
SmokerStatusEx-smoker 0.041408 0.100333 0.413 0.68001
SmokerStatusNever smoked 0.129679 0.146604 0.885 0.37684
Med.Statin.LLDyes -0.217567 0.102611 -2.120 0.03449 *
Med.all.antiplateletyes 0.274078 0.156811 1.748 0.08113 .
GFR_MDRD -0.001735 0.002490 -0.697 0.48624
BMI -0.009452 0.011971 -0.790 0.43014
MedHx_CVDyes 0.062602 0.093939 0.666 0.50547
stenose50-70% -0.247481 0.625935 -0.395 0.69274
stenose70-90% -0.044651 0.580901 -0.077 0.93876
stenose90-99% -0.018086 0.578904 -0.031 0.97509
stenose100% (Occlusion) -0.517388 0.734073 -0.705 0.48126
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.985 on 487 degrees of freedom
Multiple R-squared: 0.06665, Adjusted R-squared: 0.03598
F-statistic: 2.173 on 16 and 487 DF, p-value: 0.005318
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0.095108
Standard error............: 0.041424
Odds ratio (effect size)..: 1.1
Lower 95% CI..............: 1.014
Upper 95% CI..............: 1.193
T-value...................: 2.295945
P-value...................: 0.02210297
R^2.......................: 0.066649
Adjusted r^2..............: 0.035984
Sample size of AE DB......: 2421
Sample size of model......: 504
Missing data %............: 79.18216
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + Med.Statin.LLD + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale Hypertension.compositeyes Med.Statin.LLDyes
0.73603 -0.18374 -0.01214 0.23844 -0.19953 -0.20987
Med.all.antiplateletyes
0.30961
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.84593 -0.65886 -0.02396 0.64275 2.49905
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.283233 0.831491 1.543 0.12341
currentDF[, TRAIT] -0.183348 0.042529 -4.311 1.97e-05 ***
Age -0.015144 0.005713 -2.651 0.00829 **
Gendermale 0.243227 0.099649 2.441 0.01501 *
Hypertension.compositeyes -0.204116 0.130194 -1.568 0.11759
DiabetesStatusDiabetes -0.143792 0.110520 -1.301 0.19386
SmokerStatusEx-smoker 0.057711 0.098645 0.585 0.55880
SmokerStatusNever smoked 0.096982 0.143875 0.674 0.50059
Med.Statin.LLDyes -0.216934 0.101212 -2.143 0.03258 *
Med.all.antiplateletyes 0.270639 0.153881 1.759 0.07925 .
GFR_MDRD -0.001513 0.002444 -0.619 0.53614
BMI -0.007952 0.011730 -0.678 0.49811
MedHx_CVDyes 0.067067 0.092352 0.726 0.46806
stenose50-70% -0.201080 0.614238 -0.327 0.74353
stenose70-90% -0.032533 0.570057 -0.057 0.95451
stenose90-99% -0.025344 0.567829 -0.045 0.96442
stenose100% (Occlusion) -0.624673 0.719930 -0.868 0.38600
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9664 on 484 degrees of freedom
Multiple R-squared: 0.08617, Adjusted R-squared: 0.05596
F-statistic: 2.852 on 16 and 484 DF, p-value: 0.0001815
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.183348
Standard error............: 0.042529
Odds ratio (effect size)..: 0.832
Lower 95% CI..............: 0.766
Upper 95% CI..............: 0.905
T-value...................: -4.311152
P-value...................: 1.968754e-05
R^2.......................: 0.086169
Adjusted r^2..............: 0.05596
Sample size of AE DB......: 2421
Sample size of model......: 501
Missing data %............: 79.30607
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Med.Statin.LLD + Med.all.antiplatelet, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale Med.Statin.LLDyes Med.all.antiplateletyes
0.315167 -0.095651 -0.009008 0.321429 -0.228687 0.309419
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.13363 -0.71259 -0.01418 0.65212 2.62787
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.882235 0.851917 1.036 0.30092
currentDF[, TRAIT] -0.096252 0.054709 -1.759 0.07916 .
Age -0.011282 0.005791 -1.948 0.05198 .
Gendermale 0.329142 0.101369 3.247 0.00125 **
Hypertension.compositeyes -0.162442 0.136027 -1.194 0.23300
DiabetesStatusDiabetes -0.122953 0.114419 -1.075 0.28311
SmokerStatusEx-smoker 0.051701 0.101893 0.507 0.61211
SmokerStatusNever smoked 0.125396 0.148590 0.844 0.39915
Med.Statin.LLDyes -0.224166 0.103999 -2.155 0.03163 *
Med.all.antiplateletyes 0.274263 0.159431 1.720 0.08603 .
GFR_MDRD -0.001255 0.002555 -0.491 0.62343
BMI -0.006818 0.012141 -0.562 0.57469
MedHx_CVDyes 0.083155 0.095364 0.872 0.38366
stenose50-70% -0.323193 0.628604 -0.514 0.60739
stenose70-90% -0.039126 0.583707 -0.067 0.94659
stenose90-99% -0.033459 0.581503 -0.058 0.95414
stenose100% (Occlusion) -0.578913 0.737464 -0.785 0.43284
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9894 on 477 degrees of freedom
Multiple R-squared: 0.06118, Adjusted R-squared: 0.02969
F-statistic: 1.943 on 16 and 477 DF, p-value: 0.01535
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.096252
Standard error............: 0.054709
Odds ratio (effect size)..: 0.908
Lower 95% CI..............: 0.816
Upper 95% CI..............: 1.011
T-value...................: -1.759352
P-value...................: 0.07915876
R^2.......................: 0.061178
Adjusted r^2..............: 0.029687
Sample size of AE DB......: 2421
Sample size of model......: 494
Missing data %............: 79.59521 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Analysis of binary plaque traits as a function of serum/plaque MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.BIN)) {
TRAIT = TRAITS.BIN[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender +
Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of MCP1_pg_ug_2015_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + SmokerStatus + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age SmokerStatusEx-smoker SmokerStatusNever smoked stenose50-70% stenose70-90%
-1.16173 -0.47346 0.01896 -0.40609 -0.44306 -0.56758 -0.01349
stenose90-99% stenose100% (Occlusion) stenose50-99% stenose70-99%
0.18538 1.15456 -14.43672 -0.79787
Degrees of Freedom: 1023 Total (i.e. Null); 1013 Residual
Null Deviance: 1417
Residual Deviance: 1335 AIC: 1357
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.8447 -1.0810 -0.7025 1.1078 2.0019
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.522e+00 1.248e+00 -1.220 0.22262
currentDF[, PROTEIN] -4.740e-01 6.875e-02 -6.895 5.38e-12 ***
Age 1.797e-02 8.224e-03 2.185 0.02892 *
Gendermale -9.953e-02 1.469e-01 -0.677 0.49809
Hypertension.compositeyes 2.389e-01 2.017e-01 1.185 0.23618
DiabetesStatusDiabetes -2.266e-01 1.619e-01 -1.400 0.16160
SmokerStatusEx-smoker -4.096e-01 1.525e-01 -2.687 0.00722 **
SmokerStatusNever smoked -4.851e-01 2.157e-01 -2.249 0.02454 *
Med.Statin.LLDyes -1.636e-01 1.607e-01 -1.019 0.30842
Med.all.antiplateletyes -7.738e-02 2.248e-01 -0.344 0.73071
GFR_MDRD -5.302e-04 3.511e-03 -0.151 0.87997
BMI 2.166e-02 1.839e-02 1.178 0.23892
MedHx_CVDyes -1.660e-02 1.377e-01 -0.121 0.90406
stenose50-70% -5.216e-01 8.838e-01 -0.590 0.55510
stenose70-90% 9.803e-03 8.419e-01 0.012 0.99071
stenose90-99% 2.028e-01 8.420e-01 0.241 0.80969
stenose100% (Occlusion) 1.181e+00 1.175e+00 1.005 0.31496
stenose50-99% -1.444e+01 4.192e+02 -0.034 0.97252
stenose70-99% -7.533e-01 1.208e+00 -0.624 0.53294
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1417.5 on 1023 degrees of freedom
Residual deviance: 1329.0 on 1005 degrees of freedom
AIC: 1367
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.474031
Standard error............: 0.068749
Odds ratio (effect size)..: 0.622
Lower 95% CI..............: 0.544
Upper 95% CI..............: 0.712
Z-value...................: -6.895134
P-value...................: 5.381419e-12
Hosmer and Lemeshow r^2...: 0.062418
Cox and Snell r^2.........: 0.082776
Nagelkerke's pseudo r^2...: 0.110443
Sample size of AE DB......: 2421
Sample size of model......: 1024
Missing data %............: 57.70343
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
SmokerStatus + BMI + MedHx_CVD, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] SmokerStatusEx-smoker SmokerStatusNever smoked BMI MedHx_CVDyes
0.47638 -0.24086 -0.38304 -0.63224 0.03905 0.25051
Degrees of Freedom: 1024 Total (i.e. Null); 1019 Residual
Null Deviance: 1048
Residual Deviance: 1025 AIC: 1037
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2698 0.4534 0.6199 0.7192 1.1840
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.385e+01 9.628e+02 0.014 0.98852
currentDF[, PROTEIN] -2.330e-01 7.922e-02 -2.941 0.00327 **
Age 1.362e-02 9.731e-03 1.400 0.16155
Gendermale -4.542e-02 1.763e-01 -0.258 0.79672
Hypertension.compositeyes 2.446e-01 2.286e-01 1.070 0.28467
DiabetesStatusDiabetes 7.902e-02 1.972e-01 0.401 0.68861
SmokerStatusEx-smoker -4.387e-01 1.893e-01 -2.317 0.02051 *
SmokerStatusNever smoked -7.570e-01 2.485e-01 -3.046 0.00232 **
Med.Statin.LLDyes 2.795e-03 1.921e-01 0.015 0.98839
Med.all.antiplateletyes 2.526e-01 2.597e-01 0.973 0.33067
GFR_MDRD 5.011e-03 4.226e-03 1.186 0.23576
BMI 4.166e-02 2.314e-02 1.801 0.07175 .
MedHx_CVDyes 2.219e-01 1.635e-01 1.357 0.17468
stenose50-70% -1.480e+01 9.628e+02 -0.015 0.98774
stenose70-90% -1.509e+01 9.628e+02 -0.016 0.98750
stenose90-99% -1.519e+01 9.628e+02 -0.016 0.98741
stenose100% (Occlusion) 1.169e-01 1.242e+03 0.000 0.99992
stenose50-99% -3.410e-02 1.515e+03 0.000 0.99998
stenose70-99% -1.464e+01 9.628e+02 -0.015 0.98787
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1047.6 on 1024 degrees of freedom
Residual deviance: 1010.2 on 1006 degrees of freedom
AIC: 1048.2
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.232976
Standard error............: 0.079216
Odds ratio (effect size)..: 0.792
Lower 95% CI..............: 0.678
Upper 95% CI..............: 0.925
Z-value...................: -2.941031
P-value...................: 0.003271218
Hosmer and Lemeshow r^2...: 0.035759
Cox and Snell r^2.........: 0.035889
Nagelkerke's pseudo r^2...: 0.056063
Sample size of AE DB......: 2421
Sample size of model......: 1025
Missing data %............: 57.66212
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + SmokerStatus + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale SmokerStatusEx-smoker SmokerStatusNever smoked stenose50-70% stenose70-90%
13.8072 0.1608 0.9217 -0.2543 0.3188 -13.4987 -13.3997
stenose90-99% stenose100% (Occlusion) stenose50-99% stenose70-99%
-13.2648 -13.8075 -15.8024 -14.6344
Degrees of Freedom: 1024 Total (i.e. Null); 1014 Residual
Null Deviance: 1208
Residual Deviance: 1153 AIC: 1175
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0874 -1.2279 0.6773 0.7958 1.5145
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.323e+01 3.540e+02 0.037 0.9702
currentDF[, PROTEIN] 1.538e-01 7.309e-02 2.105 0.0353 *
Age 7.892e-03 8.954e-03 0.881 0.3781
Gendermale 9.325e-01 1.563e-01 5.966 2.43e-09 ***
Hypertension.compositeyes -3.351e-02 2.222e-01 -0.151 0.8802
DiabetesStatusDiabetes -1.599e-01 1.759e-01 -0.909 0.3634
SmokerStatusEx-smoker -2.887e-01 1.683e-01 -1.716 0.0862 .
SmokerStatusNever smoked 2.644e-01 2.474e-01 1.069 0.2852
Med.Statin.LLDyes -2.292e-01 1.845e-01 -1.242 0.2141
Med.all.antiplateletyes 1.095e-01 2.495e-01 0.439 0.6608
GFR_MDRD 4.634e-04 3.879e-03 0.119 0.9049
BMI 4.721e-03 1.983e-02 0.238 0.8118
MedHx_CVDyes 7.604e-02 1.518e-01 0.501 0.6164
stenose50-70% -1.352e+01 3.539e+02 -0.038 0.9695
stenose70-90% -1.339e+01 3.539e+02 -0.038 0.9698
stenose90-99% -1.327e+01 3.539e+02 -0.037 0.9701
stenose100% (Occlusion) -1.382e+01 3.540e+02 -0.039 0.9689
stenose50-99% -1.581e+01 3.540e+02 -0.045 0.9644
stenose70-99% -1.472e+01 3.540e+02 -0.042 0.9668
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1207.9 on 1024 degrees of freedom
Residual deviance: 1149.4 on 1006 degrees of freedom
AIC: 1187.4
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.153844
Standard error............: 0.073089
Odds ratio (effect size)..: 1.166
Lower 95% CI..............: 1.011
Upper 95% CI..............: 1.346
Z-value...................: 2.104886
P-value...................: 0.03530121
Hosmer and Lemeshow r^2...: 0.048471
Cox and Snell r^2.........: 0.055521
Nagelkerke's pseudo r^2...: 0.080203
Sample size of AE DB......: 2421
Sample size of model......: 1025
Missing data %............: 57.66212
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Med.Statin.LLD + BMI + MedHx_CVD, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Med.Statin.LLDyes BMI MedHx_CVDyes
-0.71077 -0.14275 0.51657 -0.27539 0.03056 0.36067
Degrees of Freedom: 1022 Total (i.e. Null); 1017 Residual
Null Deviance: 1367
Residual Deviance: 1337 AIC: 1349
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9526 -1.2816 0.8168 0.9931 1.5440
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.3744407 1.2806515 -0.292 0.7700
currentDF[, PROTEIN] -0.1420106 0.0661286 -2.147 0.0318 *
Age 0.0002745 0.0081696 0.034 0.9732
Gendermale 0.5755452 0.1456998 3.950 7.81e-05 ***
Hypertension.compositeyes -0.1304980 0.2014410 -0.648 0.5171
DiabetesStatusDiabetes -0.1239874 0.1610422 -0.770 0.4414
SmokerStatusEx-smoker -0.1106836 0.1537985 -0.720 0.4717
SmokerStatusNever smoked -0.1474556 0.2125316 -0.694 0.4878
Med.Statin.LLDyes -0.2598492 0.1641604 -1.583 0.1134
Med.all.antiplateletyes 0.0938507 0.2263983 0.415 0.6785
GFR_MDRD -0.0048629 0.0035299 -1.378 0.1683
BMI 0.0346557 0.0186016 1.863 0.0625 .
MedHx_CVDyes 0.3433350 0.1371721 2.503 0.0123 *
stenose50-70% -0.3005803 0.9270679 -0.324 0.7458
stenose70-90% -0.1234783 0.8929528 -0.138 0.8900
stenose90-99% 0.1233878 0.8933095 0.138 0.8901
stenose100% (Occlusion) -0.3672092 1.1204001 -0.328 0.7431
stenose50-99% -0.5109985 1.3494741 -0.379 0.7049
stenose70-99% 1.3028582 1.4085773 0.925 0.3550
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1367.4 on 1022 degrees of freedom
Residual deviance: 1325.7 on 1004 degrees of freedom
AIC: 1363.7
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IPH
Effect size...............: -0.142011
Standard error............: 0.066129
Odds ratio (effect size)..: 0.868
Lower 95% CI..............: 0.762
Upper 95% CI..............: 0.988
Z-value...................: -2.14749
P-value...................: 0.03175426
Hosmer and Lemeshow r^2...: 0.030475
Cox and Snell r^2.........: 0.039915
Nagelkerke's pseudo r^2...: 0.054139
Sample size of AE DB......: 2421
Sample size of model......: 1023
Missing data %............: 57.74473
Analysis of MCP1_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite +
DiabetesStatus + GFR_MDRD + MedHx_CVD, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Hypertension.compositeyes DiabetesStatusDiabetes GFR_MDRD MedHx_CVDyes
0.853126 0.414180 -0.390472 -0.007917 -0.353771
Degrees of Freedom: 504 Total (i.e. Null); 500 Residual
Null Deviance: 685.7
Residual Deviance: 674.9 AIC: 684.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.6720 -1.2495 0.8564 1.0272 1.5440
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.233830 1.795152 -0.687 0.4919
currentDF[, PROTEIN] -0.126459 0.094685 -1.336 0.1817
Age 0.006587 0.012009 0.548 0.5834
Gendermale -0.062235 0.212469 -0.293 0.7696
Hypertension.compositeyes 0.435742 0.275437 1.582 0.1136
DiabetesStatusDiabetes -0.443583 0.233789 -1.897 0.0578 .
SmokerStatusEx-smoker -0.185893 0.209161 -0.889 0.3741
SmokerStatusNever smoked -0.049956 0.309703 -0.161 0.8719
Med.Statin.LLDyes -0.044310 0.215971 -0.205 0.8374
Med.all.antiplateletyes 0.330244 0.328738 1.005 0.3151
GFR_MDRD -0.008275 0.005246 -1.577 0.1147
BMI 0.008342 0.025154 0.332 0.7402
MedHx_CVDyes -0.337492 0.198158 -1.703 0.0885 .
stenose50-70% 1.324815 1.348114 0.983 0.3257
stenose70-90% 1.484321 1.258815 1.179 0.2383
stenose90-99% 1.197151 1.253726 0.955 0.3396
stenose100% (Occlusion) 1.445804 1.587366 0.911 0.3624
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 685.70 on 504 degrees of freedom
Residual deviance: 667.81 on 488 degrees of freedom
AIC: 701.81
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.126459
Standard error............: 0.094685
Odds ratio (effect size)..: 0.881
Lower 95% CI..............: 0.732
Upper 95% CI..............: 1.061
Z-value...................: -1.335569
P-value...................: 0.1816902
Hosmer and Lemeshow r^2...: 0.026099
Cox and Snell r^2.........: 0.034817
Nagelkerke's pseudo r^2...: 0.046874
Sample size of AE DB......: 2421
Sample size of model......: 505
Missing data %............: 79.14085
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
SmokerStatus + Med.all.antiplatelet + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] SmokerStatusEx-smoker SmokerStatusNever smoked Med.all.antiplateletyes stenose50-70% stenose70-90%
15.3018 -0.6017 -0.5434 -0.7449 0.8164 -12.8151 -13.8459
stenose90-99% stenose100% (Occlusion)
-14.3689 -13.8620
Degrees of Freedom: 502 Total (i.e. Null); 494 Residual
Null Deviance: 498.9
Residual Deviance: 456.6 AIC: 474.6
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3896 0.3345 0.5280 0.6851 1.5020
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 15.988779 811.586839 0.020 0.9843
currentDF[, PROTEIN] -0.581541 0.126633 -4.592 4.38e-06 ***
Age -0.004520 0.015715 -0.288 0.7736
Gendermale -0.116063 0.279797 -0.415 0.6783
Hypertension.compositeyes 0.285111 0.344563 0.827 0.4080
DiabetesStatusDiabetes 0.261296 0.318063 0.822 0.4113
SmokerStatusEx-smoker -0.540833 0.279790 -1.933 0.0532 .
SmokerStatusNever smoked -0.795609 0.381493 -2.086 0.0370 *
Med.Statin.LLDyes 0.087468 0.267823 0.327 0.7440
Med.all.antiplateletyes 0.810128 0.387384 2.091 0.0365 *
GFR_MDRD -0.001096 0.006839 -0.160 0.8727
BMI -0.018480 0.033757 -0.547 0.5841
MedHx_CVDyes -0.039031 0.254256 -0.154 0.8780
stenose50-70% -12.855771 811.585748 -0.016 0.9874
stenose70-90% -13.901578 811.585085 -0.017 0.9863
stenose90-99% -14.422016 811.585069 -0.018 0.9858
stenose100% (Occlusion) -13.818364 811.586007 -0.017 0.9864
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 498.94 on 502 degrees of freedom
Residual deviance: 454.36 on 486 degrees of freedom
AIC: 488.36
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.581541
Standard error............: 0.126633
Odds ratio (effect size)..: 0.559
Lower 95% CI..............: 0.436
Upper 95% CI..............: 0.717
Z-value...................: -4.59234
P-value...................: 4.383039e-06
Hosmer and Lemeshow r^2...: 0.08935
Cox and Snell r^2.........: 0.084814
Nagelkerke's pseudo r^2...: 0.13481
Sample size of AE DB......: 2421
Sample size of model......: 503
Missing data %............: 79.22346
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Hypertension.composite + SmokerStatus, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Hypertension.compositeyes SmokerStatusEx-smoker SmokerStatusNever smoked
0.8182 0.6698 0.6858 0.6073 -0.5809 0.1511
Degrees of Freedom: 504 Total (i.e. Null); 499 Residual
Null Deviance: 502.6
Residual Deviance: 455.7 AIC: 467.7
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5819 0.3026 0.5024 0.6943 1.7559
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.348e+01 8.358e+02 0.016 0.98713
currentDF[, PROTEIN] 6.616e-01 1.307e-01 5.060 4.19e-07 ***
Age 2.063e-03 1.527e-02 0.135 0.89255
Gendermale 6.790e-01 2.574e-01 2.638 0.00834 **
Hypertension.compositeyes 6.101e-01 3.385e-01 1.802 0.07151 .
DiabetesStatusDiabetes -2.782e-01 2.925e-01 -0.951 0.34152
SmokerStatusEx-smoker -6.015e-01 2.746e-01 -2.190 0.02849 *
SmokerStatusNever smoked 7.161e-02 4.340e-01 0.165 0.86895
Med.Statin.LLDyes -1.998e-01 2.884e-01 -0.693 0.48837
Med.all.antiplateletyes 7.136e-02 4.091e-01 0.174 0.86155
GFR_MDRD 4.457e-04 6.890e-03 0.065 0.94842
BMI 3.568e-02 3.222e-02 1.108 0.26800
MedHx_CVDyes 4.673e-02 2.516e-01 0.186 0.85264
stenose50-70% -1.445e+01 8.358e+02 -0.017 0.98621
stenose70-90% -1.341e+01 8.358e+02 -0.016 0.98720
stenose90-99% -1.375e+01 8.358e+02 -0.016 0.98688
stenose100% (Occlusion) -1.330e+01 8.358e+02 -0.016 0.98731
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 502.62 on 504 degrees of freedom
Residual deviance: 448.07 on 488 degrees of freedom
AIC: 482.07
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.661562
Standard error............: 0.130738
Odds ratio (effect size)..: 1.938
Lower 95% CI..............: 1.5
Upper 95% CI..............: 2.504
Z-value...................: 5.060229
P-value...................: 4.187523e-07
Hosmer and Lemeshow r^2...: 0.108531
Cox and Snell r^2.........: 0.10239
Nagelkerke's pseudo r^2...: 0.162425
Sample size of AE DB......: 2421
Sample size of model......: 505
Missing data %............: 79.14085
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender +
DiabetesStatus + BMI + MedHx_CVD, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age Gendermale DiabetesStatusDiabetes BMI MedHx_CVDyes
-2.26557 0.02226 0.70174 -0.53751 0.04947 0.39322
Degrees of Freedom: 504 Total (i.e. Null); 499 Residual
Null Deviance: 563
Residual Deviance: 539.1 AIC: 551.1
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1330 0.4231 0.6238 0.7716 1.4388
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.086040 1.954689 -1.579 0.11438
currentDF[, PROTEIN] 0.101448 0.109790 0.924 0.35548
Age 0.020151 0.013750 1.465 0.14280
Gendermale 0.712360 0.232134 3.069 0.00215 **
Hypertension.compositeyes 0.309453 0.305779 1.012 0.31153
DiabetesStatusDiabetes -0.548487 0.259659 -2.112 0.03466 *
SmokerStatusEx-smoker -0.148965 0.244601 -0.609 0.54252
SmokerStatusNever smoked -0.028524 0.356456 -0.080 0.93622
Med.Statin.LLDyes -0.040642 0.254633 -0.160 0.87319
Med.all.antiplateletyes -0.213131 0.397093 -0.537 0.59146
GFR_MDRD -0.003947 0.006152 -0.642 0.52119
BMI 0.050067 0.029126 1.719 0.08562 .
MedHx_CVDyes 0.383492 0.222017 1.727 0.08411 .
stenose50-70% 1.296810 1.374938 0.943 0.34559
stenose70-90% 1.176546 1.261872 0.932 0.35114
stenose90-99% 1.344922 1.257984 1.069 0.28502
stenose100% (Occlusion) 1.411454 1.714384 0.823 0.41034
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 562.96 on 504 degrees of freedom
Residual deviance: 534.60 on 488 degrees of freedom
AIC: 568.6
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IPH
Effect size...............: 0.101448
Standard error............: 0.10979
Odds ratio (effect size)..: 1.107
Lower 95% CI..............: 0.892
Upper 95% CI..............: 1.373
Z-value...................: 0.924013
P-value...................: 0.3554794
Hosmer and Lemeshow r^2...: 0.050381
Cox and Snell r^2.........: 0.054615
Nagelkerke's pseudo r^2...: 0.081271
Sample size of AE DB......: 2421
Sample size of model......: 505
Missing data %............: 79.14085 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
B. Cross-sectional analysis symptoms
We will perform a cross-sectional analysis between plaque and serum MCP1, IL6, and IL6R levels and the ‘clinical status’ of the plaque in terms of presence of patients’ symptoms (symptomatic vs. asymptomatic). The symptoms of interest are:
- stroke
- TIA
- retinal infarction
- amaurosis fugax
- asymptomatic
Model 1
In this model we correct for Age, and Gender.
Inverse-rank normalization
Here we use the inverse-rank normalized data - visually this is more normally distributed.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
TRAIT = "AsymptSympt"
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
# + Hypertension.composite + DiabetesStatus + SmokerCurrent +
# Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
# CAD_history + Stroke_history + Peripheral.interv + stenose
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
Analysis of MCP1_pg_ug_2015_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale
0.24776 0.26397 0.03239 -0.43507
Degrees of Freedom: 1195 Total (i.e. Null); 1192 Residual
Null Deviance: 826.5
Residual Deviance: 804.7 AIC: 812.7
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5654 0.3738 0.4440 0.5167 0.8434
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.24776 0.69360 0.357 0.72093
currentDF[, PROTEIN] 0.26397 0.09376 2.815 0.00487 **
Age 0.03239 0.01011 3.203 0.00136 **
Gendermale -0.43507 0.21792 -1.996 0.04588 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 826.52 on 1195 degrees of freedom
Residual deviance: 804.69 on 1192 degrees of freedom
AIC: 812.69
Number of Fisher Scoring iterations: 5
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.263965
Standard error............: 0.09376
Odds ratio (effect size)..: 1.302
Lower 95% CI..............: 1.083
Upper 95% CI..............: 1.565
Z-value...................: 2.815333
P-value...................: 0.004872671
Hosmer and Lemeshow r^2...: 0.026418
Cox and Snell r^2.........: 0.018091
Nagelkerke's pseudo r^2...: 0.036257
Sample size of AE DB......: 2421
Sample size of model......: 1196
Missing data %............: 50.59893
Analysis of MCP1_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age
0.28675 0.37178 0.02121
Degrees of Freedom: 564 Total (i.e. Null); 562 Residual
Null Deviance: 495.5
Residual Deviance: 483.5 AIC: 489.5
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3596 0.4499 0.5370 0.6291 0.9789
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.44218 0.89858 0.492 0.62265
currentDF[, PROTEIN] 0.38662 0.12065 3.205 0.00135 **
Age 0.02149 0.01316 1.633 0.10250
Gendermale -0.23867 0.26432 -0.903 0.36656
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 495.50 on 564 degrees of freedom
Residual deviance: 482.64 on 561 degrees of freedom
AIC: 490.64
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.386619
Standard error............: 0.120648
Odds ratio (effect size)..: 1.472
Lower 95% CI..............: 1.162
Upper 95% CI..............: 1.865
Z-value...................: 3.204514
P-value...................: 0.001352907
Hosmer and Lemeshow r^2...: 0.02596
Cox and Snell r^2.........: 0.022509
Nagelkerke's pseudo r^2...: 0.038546
Sample size of AE DB......: 2421
Sample size of model......: 565
Missing data %............: 76.66254 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.Symptoms")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Model 2
In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis..
Inverse-rank normalization
Here we use the inverse-rank normalized data - visually this is more normally distributed.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
TRAIT = "AsymptSympt"
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + + stenose,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
Analysis of MCP1_pg_ug_2015_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Med.all.antiplatelet + GFR_MDRD + stenose,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale Med.all.antiplateletyes GFR_MDRD stenose50-70%
15.334896 0.272930 0.032194 -0.427140 -0.929815 0.007921 -13.442880
stenose70-90% stenose90-99% stenose100% (Occlusion) stenose50-99% stenose70-99%
-15.004949 -14.749153 -0.339553 -15.814682 -0.541412
Degrees of Freedom: 1035 Total (i.e. Null); 1024 Residual
Null Deviance: 726.5
Residual Deviance: 689.3 AIC: 713.3
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + +stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.0446 0.3116 0.4390 0.5442 0.9328
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.593e+01 9.540e+02 0.017 0.9867
currentDF[, PROTEIN] 2.558e-01 1.018e-01 2.512 0.0120 *
Age 3.508e-02 1.258e-02 2.788 0.0053 **
Gendermale -3.507e-01 2.366e-01 -1.482 0.1383
Hypertension.compositeyes -3.437e-01 3.443e-01 -0.998 0.3182
DiabetesStatusDiabetes -3.937e-02 2.423e-01 -0.162 0.8709
SmokerStatusEx-smoker -3.416e-01 2.335e-01 -1.463 0.1434
SmokerStatusNever smoked 2.040e-04 3.564e-01 0.001 0.9995
Med.Statin.LLDyes -1.797e-01 2.660e-01 -0.676 0.4991
Med.all.antiplateletyes -9.093e-01 4.789e-01 -1.899 0.0576 .
GFR_MDRD 7.160e-03 5.456e-03 1.312 0.1894
BMI -1.027e-02 2.789e-02 -0.368 0.7128
MedHx_CVDyes 9.204e-02 2.096e-01 0.439 0.6606
stenose50-70% -1.338e+01 9.540e+02 -0.014 0.9888
stenose70-90% -1.495e+01 9.540e+02 -0.016 0.9875
stenose90-99% -1.472e+01 9.540e+02 -0.015 0.9877
stenose100% (Occlusion) -3.915e-01 1.230e+03 0.000 0.9997
stenose50-99% -1.592e+01 9.540e+02 -0.017 0.9867
stenose70-99% -5.044e-01 1.190e+03 0.000 0.9997
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 726.47 on 1035 degrees of freedom
Residual deviance: 684.24 on 1017 degrees of freedom
AIC: 722.24
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.255797
Standard error............: 0.10185
Odds ratio (effect size)..: 1.291
Lower 95% CI..............: 1.058
Upper 95% CI..............: 1.577
Z-value...................: 2.511506
P-value...................: 0.01202173
Hosmer and Lemeshow r^2...: 0.058134
Cox and Snell r^2.........: 0.039945
Nagelkerke's pseudo r^2...: 0.079253
Sample size of AE DB......: 2421
Sample size of model......: 1036
Missing data %............: 57.20777
Analysis of MCP1_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + GFR_MDRD, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age GFR_MDRD
-0.724208 0.329443 0.024980 0.009997
Degrees of Freedom: 504 Total (i.e. Null); 501 Residual
Null Deviance: 454.5
Residual Deviance: 443.5 AIC: 451.5
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + +stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3662 0.3746 0.5241 0.6434 1.1320
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.533e+01 1.370e+03 0.011 0.99107
currentDF[, PROTEIN] 3.706e-01 1.278e-01 2.899 0.00374 **
Age 2.766e-02 1.564e-02 1.769 0.07693 .
Gendermale -2.995e-01 2.855e-01 -1.049 0.29414
Hypertension.compositeyes -4.287e-01 4.214e-01 -1.017 0.30894
DiabetesStatusDiabetes 1.626e-01 3.125e-01 0.520 0.60277
SmokerStatusEx-smoker -2.245e-01 2.817e-01 -0.797 0.42538
SmokerStatusNever smoked -4.152e-01 3.932e-01 -1.056 0.29091
Med.Statin.LLDyes -2.065e-01 3.017e-01 -0.684 0.49380
Med.all.antiplateletyes -6.728e-01 5.088e-01 -1.322 0.18605
GFR_MDRD 1.021e-02 6.899e-03 1.480 0.13884
BMI 1.484e-02 3.326e-02 0.446 0.65545
MedHx_CVDyes 1.198e-01 2.579e-01 0.465 0.64217
stenose50-70% -1.390e+01 1.370e+03 -0.010 0.99190
stenose70-90% -1.542e+01 1.370e+03 -0.011 0.99102
stenose90-99% -1.512e+01 1.370e+03 -0.011 0.99119
stenose100% (Occlusion) -2.746e-01 1.705e+03 0.000 0.99987
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 454.53 on 504 degrees of freedom
Residual deviance: 430.04 on 488 degrees of freedom
AIC: 464.04
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.370558
Standard error............: 0.127809
Odds ratio (effect size)..: 1.449
Lower 95% CI..............: 1.128
Upper 95% CI..............: 1.861
Z-value...................: 2.899318
P-value...................: 0.003739752
Hosmer and Lemeshow r^2...: 0.053877
Cox and Snell r^2.........: 0.047336
Nagelkerke's pseudo r^2...: 0.079763
Sample size of AE DB......: 2421
Sample size of model......: 505
Missing data %............: 79.14085 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
C. Longitudinal analysis secondary clinical outcome
For the longitudinal analyses of plaque and serum MCP1, IL6, and IL6R levels and secondary cardiovascular events over a three-year follow-up period.
The primary outcome is defined as “a composite of fatal or non-fatal myocardial infarction, fatal or non-fatal stroke, ruptured aortic aneurysm, fatal cardiac failure, coronary or peripheral interventions, leg amputation due to vascular causes, and cardiovascular death”, i.e. major adverse cardiovascular events (MACE). Variable: epmajor.3years, these include: - myocardial infarction (MI) - cerebral infarction (CVA/stroke) - cardiovascular death (exact cause to be investigated) - cerebral bleeding (CVA/stroke) - fatal myocardial infarction (MI) - fatal cerebral infarction - fatal cerebral bleeding - sudden death - fatal heart failure - fatal aneurysm rupture - other cardiovascular death..
The secondary outcomes will be
- incidence of fatal or non-fatal stroke (ischemic and bleeding) - variable:
epstroke.3years, these include:- cerebral infarction (CVA/stroke)
- cerebral bleeding (CVA/stroke)
- fatal cerebral infarction
- fatal cerebral bleeding.
- incidence of acute coronary events (fatal or non-fatal myocardial infarction, coronary interventions) - variable:
epcoronary.3years, these include:- myocardial infarction (MI)
- coronary angioplasty (PCI/PTCA)
- cardiovascular death (exact cause to be investigated)
- coronary bypass (CABG)
- fatal myocardial infarction (MI)
- sudden death.
- cardiovascular death - variable:
epcvdeath.3years, these include:- cardiovascular death (exact cause to be investigated)
- fatal myocardial infarction (MI)
- fatal cerebral infarction
- fatal cerebral bleeding
- sudden death
- fatal heart failure
- fatal aneurysm rupture
- other cardiovascular death..
30- and 90-days FU events
We will use 3-year follow-up, but we will also calculate 30 days and 90 days follow-up ‘time-to-event’ variables. On average there are 365.25 days in a year. We can calculate 30-days and 90-days follow-up time based on the three years follow-up.
cutt.off.30days = (1/365.25) * 30
cutt.off.90days = (1/365.25) * 90
# Fix maximum FU of 30 and 90 days
AEDB <- AEDB %>%
mutate(
FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
)
AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary",
"max.followup",
"FU.cutt.off.3years",
"FU.cutt.off.30days",
"FU.cutt.off.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
rm(AEDB.temp)
AEDB.CEA <- AEDB.CEA %>%
mutate(
FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
)
AEDB.CEA.temp <- subset(AEDB.CEA, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary",
"max.followup",
"FU.cutt.off.3years",
"FU.cutt.off.30days",
"FU.cutt.off.90days"))
require(labelled)
AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)
DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)
rm(AEDB.CEA.temp)
Here we will calculate the new 30- and 90-days follow-up of the events and their event-times of interest:
- MACE (
epmajor.3years) - Stroke (
epstroke.3years) - Coronary events (
epcoronary.3years) - Cardiovascular death (
epcvdeath.3years)
avg_days_in_year = 365.25
cutt.off.30days.scaled <- cutt.off.30days * 365.25
cutt.off.90days.scaled <- cutt.off.90days * 365.25
# Event times
AEDB <- AEDB %>%
mutate(
ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
)
AEDB.CEA <- AEDB.CEA %>%
mutate(
ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
)
attach(AEDB)The following objects are masked from AEDB (pos = 3):
ABI_70, ABI_max, ABI_mean, ABI_min, ABI_OP, ablock, ablock2, ablock3, aceinhib, aceinhib2, acetylsa, Adiponectin_pg_ug_2015, AE_AAA_bijzonderheden, Age, Age_Q,
AgeSQR, aid, Aldosteron_recode, alg10201, alg10202, alg10203, alg10204, alg10205, alg105, alg106, alg109, alg110, alg113, alg114, alg115, ALOX5, analg2, analg3,
analgeti, Ang2, angioii, ANGPT2, anti_apoA1_IgG, anti_apoA1_index, anti_apoA1_na, antiall, antiall2, antiarrh, antiarrh2, ANXA2, AP_Dx, AP_Dx1, AP_Dx2, APOB,
artercon, Artery_summary, arteryop, AsymptSympt, AsymptSympt2G, bblock, bblock2, blocko, blocksnr, BMI, BMI_US, BMI_WHO, BMI30ormore, brain401, brain402, brain403,
brain404, brain405, brain406, brain407, brain408, brain409, brain410, brain411, brain412, brain413, brn40701, bspoed, CAD_Dx, CAD_Dx1, CAD_Dx2, CAD_history,
CADPAOD_history, Calc.bin, calcification, calcium, calcium2, calreg, carbasal, cardioembolic, Caspase3_7, CAV1, CD44, CD44V3, CEA_or_CAS, CEL, CFD_recalc, cholverl,
cholverl2, cholverl3, CI_history, clau1, clau2, Claudication, clopidog, CML, collagen, Collagen.bin, combi1, combi2, combi3, comorbidity.DM, concablo, concablo2,
concablo3, concace2, concacei, concacet, concalle, concanal, concanal2, concanal3, concangi, concanta2, concanti, concanti2, concbblo, concbblo2, conccalc,
conccalc2, conccalreg, conccarb, concchol, concchol2, concchol3, concclau1, concclau2, concclop, conccom1, conccom2, conccom3, conccort, conccorthorm2, concderm,
concdig, concdig2, concdig3, concdig4, concdipy, concdiur, concdiur2, concdiur3, concerec, conceye, concgluc, concgluc2, concgluc3, concgluc4, concgrel, concinsu,
conciron, conciron2, concneur, concneur2, concneur3, concneur4, concnitr, concnitr2, concotant, concotcor, concoth2, concothe, concpros, concpsy5, concren,
concresp, concrheu, concrheu2, concrheu3, concsta2, concstat, concthro, concthyr, concthyr2, concvit2, concvita, Contralateral_surgery, conwhen, corticos,
cortihorm2, creat, crp_all, CRP_avg, CRP_dif, crp_source, CRP_var, CST3_pg_ug, CST3_serum_luminex, CTGF, cTNI_plasma, CTSA, CTSB, CTSL1, CTSS, cyr61,
date_ic_patient, date_ic_researcher, Date.of.birth, date.previous.operation, date1yr, date3mon, dateapprox_latest, dateapprox_worst, dateapprox1, dateapprox2,
dateapprox3, dateapprox4, dateend1, dateend2, dateend3, dateend4, dateend5, dateend6, dateexact_latest, dateexact_worst, dateexact1, dateexact2, dateexact3,
dateexact4, dateok, dermacor, DiabetesStatus, diastoli, diet801, diet802, diet803, diet804, diet805, diet806, diet807, diet808, diet809, diet810, diet811, diet812,
diet813, diet814, diet815, diet816, diet817, diet818, diet819, diet820, diet821, diet822, diet823, diet824, dipyridi, diuretic, diuretic2, diuretic3, DM,
DM.composite, duaalantiplatelet, duplend, eaindexl, eaindexr, eCigarettes, edaplaqu_recalc, edavrspl, EGR, EMMPRIN_45kD, EMMPRIN_58kD, ENDOGLIN, endpoint1,
endpoint2, endpoint3, endpoint4, endpoint5, endpoint6, Eotaxin1, EP_CAD, ep_cad_t_30days, ep_cad_t_3years, EP_CAD_time, ep_cad.30days, EP_CI, ep_ci_t_30days,
ep_ci_t_3years, EP_CI_time, ep_com_t_30days, ep_com_t_3years, EP_composite, EP_composite_time, EP_coronary, ep_coronary_t_3years, EP_coronary_time, EP_CVdeath,
ep_cvdeath_t_30days, ep_cvdeath_t_3years, EP_CVdeath_time, EP_death, ep_death_t_30days, ep_death_t_3years, EP_death_time, EP_fatalCVA, ep_fatalCVA_t_30days,
ep_fatalCVA_t_3years, EP_fatalCVA_time, EP_hemorrhagic_stroke, ep_hemorrhagic_stroke_t_3years, EP_hemorrhagic_stroke_time, ep_hemorrhagic_stroke.3years,
EP_ischemic_stroke, ep_ischemic_stroke_t_3years, EP_ischemic_stroke_time, ep_ischemic_stroke.3years, EP_leg_amputation, EP_leg_amputation_time,
ep_legamputation_t_30days, ep_legamputation_t_3years, EP_major, ep_major_t_30days, ep_major_t_3years, EP_major_time, EP_MI, ep_mi_t_30days, ep_mi_t_3years,
EP_MI_time, EP_nonstroke_event, EP_nonstroke_event_time, ep_nonstroke_t_3years, EP_peripheral, ep_peripheral_t_30days, ep_peripheral_t_3years, EP_peripheral_time,
EP_pta, ep_pta_t_30days, ep_pta_t_3years, EP_pta_time, EP_stroke, ep_stroke_t_30days, ep_stroke_t_3years, EP_stroke_time, EP_strokeCVdeath,
ep_strokeCVdeath_t_30days, ep_strokeCVdeath_t_3years, EP_strokeCVdeath_time, EP_strokedeath, ep_strokedeath_t_30days, ep_strokedeath_t_3years, EP_strokedeath_time,
ePackYearsSmoking, epcad.3years, epci.30days, epci.3years, epcom.30days, epcom.3years, epcoronary.3years, epcvdeath.30days, epcvdeath.3years, epdeath.30days,
epdeath.3years, epfatalCVA.30days, epfatalCVA.3years, eplegamputation.30days, eplegamputation.3years, epmajor.30days, epmajor.3years, epmi.30days, epmi.3years,
epnonstroke.3years, epperipheral.30days, epperipheral.3years, eppta.30days, eppta.3years, epstroke.30days, epstroke.3years, epstrokeCVdeath.30days,
epstrokeCVdeath.3years, epstrokedeath.30days, epstrokedeath.3years, erec, Estradiol, everstroke_composite, Everstroke_Ipsilateral, exer901, exer902, exer903,
exer904, exer905, exer906, exer9071, exer9072, exer9073, exer9074, exer9075, exer9076, exer908, exer909, exer910, eyedrop, EZis, FABP_serum, FABP4, FABP4_pg_ug,
FABP4_serum_luminex, fat, Fat.bin_10, Fat.bin_40, Femoral.interv, FH_AAA_broth, FH_AAA_comp, FH_AAA_mat, FH_AAA_parent, FH_AAA_pat, FH_AAA_sibling, FH_AAA_sis,
FH_amp_broth, FH_amp_comp, FH_amp_mat, FH_amp_parent, FH_amp_pat, FH_amp_sibling, FH_amp_sis, FH_CAD_broth, FH_CAD_comp, FH_CAD_mat, FH_CAD_parent, FH_CAD_pat,
FH_CAD_sibling, FH_CAD_sis, FH_corcalc_broth, FH_corcalc_comp, FH_corcalc_mat, FH_corcalc_parent, FH_corcalc_pat, FH_corcalc_sibling, FH_corcalc_sis, FH_CVD_broth,
FH_CVD_comp, FH_CVD_mat, FH_CVD_parent, FH_CVD_pat, FH_CVD_sibling, FH_CVD_sis, FH_CVdeath_broth, FH_CVdeath_comp, FH_CVdeath_mat, FH_CVdeath_parent,
FH_CVdeath_pat, FH_CVdeath_sibling, FH_CVdeath_sis, FH_DM_broth, FH_DM_comp, FH_DM_mat, FH_DM_parent, FH_DM_pat, FH_DM_sibling, FH_DM_sis, FH_HC_broth, FH_HC_comp,
FH_HC_mat, FH_HC_parent, FH_HC_pat, FH_HC_sibling, FH_HC_sis, FH_HT_broth, FH_HT_comp, FH_HT_mat, FH_HT_parent, FH_HT_pat, FH_HT_sibling, FH_HT_sis, FH_MI_broth,
FH_MI_comp, FH_MI_mat, FH_MI_parent, FH_MI_pat, FH_MI_sibling, FH_MI_sis, FH_otherCVD_broth, FH_otherCVD_comp, FH_otherCVD_mat, FH_otherCVD_parent, FH_otherCVD_pat,
FH_otherCVD_sibling, FH_otherCVD_sis, FH_PAD_broth, FH_PAD_comp, FH_PAD_mat, FH_PAD_parent, FH_PAD_pat, FH_PAD_sibling, FH_PAD_sis, FH_PAV_broth, FH_PAV_comp,
FH_PAV_mat, FH_PAV_parent, FH_PAV_pat, FH_PAV_sibling, FH_PAV_sis, FH_POB_broth, FH_POB_comp, FH_POB_mat, FH_POB_parent, FH_POB_pat, FH_POB_sibling, FH_POB_sis,
FH_risk_broth, FH_risk_comp, FH_risk_mat, FH_risk_parent, FH_risk_pat, FH_risk_sibling, FH_risk_sis, FH_Stroke_broth, FH_Stroke_comp, FH_Stroke_mat,
FH_Stroke_parent, FH_Stroke_pat, FH_Stroke_sibling, FH_Stroke_sis, FH_tromb_broth, FH_tromb_comp, FH_tromb_mat, FH_tromb_parent, FH_tromb_pat, FH_tromb_sibling,
FH_tromb_sis, filter_$, folicaci, followup1, followup2, followup3, Fontaine, FU_check, FU_check_date, FU.cutt.off.30days, FU.cutt.off.3years, FU1JAAR, FU2JAAR,
FU3JAAR, FURIN_low, FURIN_up, GDF15_plasma, geen_med, Gender, GFR_CG, GFR_MDRD, glucose, GR_Segment, GrB_plaque, GrB_serum, grel, GrK_plaque, GrK_serum, GrM_plaque,
GrM_serum, HA, hb, HDAC9, HDL, HDL_2016, HDL_all, HDL_avg, HDL_clinic, HDL_dif, HDL_final, HDL_finalCU, hdl_source, HDL_var, heart300, heart301, heart302, heart303,
heart304, heart305, heart306, heart307, heart308, heart309, heart310, heart311, heart312, heart313, heart314, heart315, heart316, heart317, heart318, heart319,
heart320, heart321, heart322, heart323, heart324, heart325, heart326, heart327, heart328, HIF1A, ho1, homocys, Hospital, hrt31301, hsCRP_plasma, ht, HYAL55KD,
HYALURON, Hypertension.composite, Hypertension.drugs, Hypertension1, Hypertension2, IL1_Beta, IL10, IL12, IL13, IL17, IL2, IL21, IL4, IL5, IL6, IL6_pg_ug_2015,
IL6R_pg_ug_2015, IL8, IL8_pg_ug_2015, IL9, indexsymptoms_latest, indexsymptoms_latest_4g, indexsymptoms_worst, indexsymptoms_worst_4g, INFG, informedconsent,
insulin, insuline, INVULDAT, IP10, IPH_extended.bin, IPH.bin, ironfoli, ironfoli2, KDOQI, latest, LDL, LDL_2016, LDL_all, LDL_avg, LDL_clinic, LDL_dif, LDL_final,
LDL_finalCU, ldl_source, LDL_var, leg501, leg502, leg503, leg504, leg505, leg506, leg507, leg508, leg509, leg510, leg511, leg512, leg513, leg514, leg515, leg516,
leg517, leg518, leg519, leg520, LMW1STME, LTB4, LTB4R, macmean0, macrophages, macrophages_location, Macrophages.bin, MAP, Mast_cells_plaque, max.followup, MCP1,
MCP1_pg_ug_2015, MCSF_pg_ug_2015, MDC, Med_notes, Med.ablock, Med.ACE_inh, Med.acetylsal, Med.acetylsal_Combi1, Med.acetylsal_Combi2, Med.acetylsal_Combi3,
Med.ADPinh, Med.all.antiplatelet, Med.angiot2.antag, Med.antiarrh, Med.anticoagulants, Med.ascal, Med.aspirin.derived, Med.bblocker, Med.calc_antag,
Med.dipyridamole, Med.diuretic, Med.LLD, Med.nitrate, Med.otheranthyp, Med.renin, Med.statin, Med.statin.derived, Med.Statin.LLD, Med.statin2, media, MG_H1, MI_Dx,
MI_Dx1, MI_Dx2, MIF, MIG, MIP1a, miRNA100_RNU19, miRNA100_RNU48, miRNA155_RNU19, miRNA155_RNU48, MMP14, MMP2, MMP2TIMP2, MMP8, MMP9, MMP9TIMP1, MPO_plasma, MRP_14,
MRP_8, MRP_8_14C, MRP_8_14C_buhlmann, MRP14_plasma, MRP8_14C_plasma, MRP8_plasma, negatibl, neuropsy, neuropsy2, neuropsy3, neuropsy4, neurpsy5, neutrophils, NGAL,
NGAL_low, NGAL_MMP9_complex, NGAL_MMP9_local, NGAL_MMP9_peripheral, NGAL_total, NGAL_up, nitrate, nitrate2, NOD1, NOD2, nogobt1_recalculated, NTproBNP_plasma,
Number_Events_Sorter, Number_Sorted_CD14, Number_Sorted_CD20, Number_Sorted_CD4_Cells, Number_Sorted_CD8_Cells, oac701, oac702, oac70305, oac704, oac705, oac706,
oac707, oac708, oac709, oac710, oac711, oac712, oac713, oac714, OKyear, OPG, OPG_plasma, OPN, OPN_2013, OPN_plasma, OR_blood, Oral.glucose.inh, oralgluc, oralgluc2,
oralgluc3, oralgluc4, ORyear, othanthyp, othcoron, other, other2, OverallPlaquePhenotype, PAI1_pg_ug_2015, PAOD, PARC, patch, PCSK9_plasma, PDGF_BB_plasma,
Percentage_CD14, Percentage_CD20, Percentage_CD4, Percentage_CD8, Peripheral.interv, PKC, PLA2_plasma, plaquephenotype, positibl, PrimaryLast, PrimaryLast1,
prostagl, PulsePressure, qual01, qual02, qual0301, qual0302, qual0303, qual0304, qual0305, qual0306, qual0307, qual0308, qual0309, qual0310, qual0401, qual0402,
qual0403, qual0404, qual0501, qual0502, qual0503, qual06, qual07, qual08, qual0901, qual0902, qual0903, qual0904, qual0905, qual0906, qual0907, qual0908, qual0909,
qual1010, qual1101, qual1102, qual1103, qual1104, RAAS_med, RANTES, RANTES_pg_ug_2015, RANTES_plasma, Ras, RE50_01, RE70_01, Renine_recode, renineinh, restenos,
restenosisOK, rheuma, rheuma2, rheuma3, risk601, risk602, risk603, risk604, risk605, risk606, risk607, risk608, risk609, risk610, risk611, risk612, risk613,
risk614, risk615, risk616, risk617, risk618, risk619, risk620, SHBG, sICAM1, SMAD1_5_8, SMAD2, SMAD3, smc, smc_location, smc_macrophages_ratio, SMC.bin, smcmean0,
SmokerCurrent, SmokingReported, SmokingYearOR, stat3P, statin2, statines, ste3mext, sten1yr, sten3mo, stenose, stenosis_con_bin, Stenosis_contralateral,
Stenosis_ipsilateral, Stroke_Dx, Stroke_eitherside, Stroke_history, Stroke_Symptoms, StrokeTIA_Dx, StrokeTIA_history, StrokeTIA_Symptoms, STUDY_NUMBER, sympt,
Sympt_latest, Sympt_worst, sympt1, sympt2, sympt3, sympt4, Symptoms.3g, Symptoms.4g, Symptoms.5G, systolic, T_NUMBER, TARC, TAT_plasma, TC_2016, TC_all, TC_avg,
TC_clinic, TC_dif, TC_final, TC_finalCU, TC_var, Testosterone, TG_2016, TG_all, TG_avg, TG_clinic, TG_dif, TG_final, TG_finalCU, TG_var, TGF, TGFB, thrombos,
thrombus, thrombus_location, thrombus_new, thrombus_organization, thrombus_organization_v2, thrombus_percentage, thyros2, thyrosta, Time_event_OR, TimeOR_latest,
TimeOR_latest_4g, TimeOR_worst, TimeOR_worst_4g, TIMP1, TIMP2, TISNOW, TNFA, totalchol, totalcholesterol_source, tractdig, tractdig2, tractdig3, tractdig4,
tractres, Treatment.DM, TREM1, triglyceride_source, triglyceriden, Trop1, Trop1DT, Trop2, Trop2DT, Trop3, Trop3DT, TropmaxpostOK, TropoMax, TropoMaxDT,
tropomaxpositief, TSratio_blood, TSratio_plaque, UPID, validation_date, validation1, validation2, validation3, validation4, validation5, validation6, VAR00001,
VEFGA, VEGFA_plasma, vegfa422, vessel_density, vessel_density_additional, vessel_density_averaged, vessel_density_Timo2012, vessel_density_Timo2012_2,
vessel_density_Timo2013, vitamin, vitamin2, vitb12, VRAGENLIJST, vWF_plasma, WBC_THAW, Which.femoral.artery, Whichoperation, writtenIC, yearablo, yearablo2,
yearablo3, yearace, yearace2, yearacet, yearanal, yearanal2, yearanal3, yearangi, yearanta, yearanta2, yearanti, yearanti2, yearbblo, yearbblo2, yearcalc,
yearcalc2, yearcalreg, yearcarb, yearchol, yearchol2, yearchol3, yearclau1, yearclau2, yearclop, yearcom1, yearcom2, yearcom3, yearcort, yearcorthorm2, yearderm,
yeardig, yeardig2, yeardig3, yeardig4, yeardipy, yeardiur, yeardiur2, yeardiur3, yearerec, yeareye, yeargluc, yeargluc2, yeargluc3, yeargluc4, yeargrel, yearinsu,
yeariron, yeariron2, yearneur, yearneur2, yearneur3, yearneur4, yearnitr, yearnitr2, yearOR_bin_2010, YearOR_per2years, yearotant, yearotcor, yearoth2, yearothe,
yearpros, yearpsy5, yearren, yearresp, yearrheu, yearrheu2, yearrheu3, yearsta2, yearstat, yearthro, yearthyr, yearthyr2, yearvit2, yearvita, Yrs.no.smoking,
Yrs.smoking
The following objects are masked from AEDB (pos = 4):
ABI_70, ABI_max, ABI_mean, ABI_min, ABI_OP, ablock, ablock2, ablock3, aceinhib, aceinhib2, acetylsa, Adiponectin_pg_ug_2015, AE_AAA_bijzonderheden, Age, Age_Q,
AgeSQR, aid, Aldosteron_recode, alg10201, alg10202, alg10203, alg10204, alg10205, alg105, alg106, alg109, alg110, alg113, alg114, alg115, ALOX5, analg2, analg3,
analgeti, Ang2, angioii, ANGPT2, anti_apoA1_IgG, anti_apoA1_index, anti_apoA1_na, antiall, antiall2, antiarrh, antiarrh2, ANXA2, AP_Dx, AP_Dx1, AP_Dx2, APOB,
artercon, Artery_summary, arteryop, AsymptSympt, AsymptSympt2G, bblock, bblock2, blocko, blocksnr, BMI, BMI_US, BMI_WHO, BMI30ormore, brain401, brain402, brain403,
brain404, brain405, brain406, brain407, brain408, brain409, brain410, brain411, brain412, brain413, brn40701, bspoed, CAD_Dx, CAD_Dx1, CAD_Dx2, CAD_history,
CADPAOD_history, Calc.bin, calcification, calcium, calcium2, calreg, carbasal, cardioembolic, Caspase3_7, CAV1, CD44, CD44V3, CEA_or_CAS, CEL, CFD_recalc, cholverl,
cholverl2, cholverl3, CI_history, clau1, clau2, Claudication, clopidog, CML, collagen, Collagen.bin, combi1, combi2, combi3, comorbidity.DM, concablo, concablo2,
concablo3, concace2, concacei, concacet, concalle, concanal, concanal2, concanal3, concangi, concanta2, concanti, concanti2, concbblo, concbblo2, conccalc,
conccalc2, conccalreg, conccarb, concchol, concchol2, concchol3, concclau1, concclau2, concclop, conccom1, conccom2, conccom3, conccort, conccorthorm2, concderm,
concdig, concdig2, concdig3, concdig4, concdipy, concdiur, concdiur2, concdiur3, concerec, conceye, concgluc, concgluc2, concgluc3, concgluc4, concgrel, concinsu,
conciron, conciron2, concneur, concneur2, concneur3, concneur4, concnitr, concnitr2, concotant, concotcor, concoth2, concothe, concpros, concpsy5, concren,
concresp, concrheu, concrheu2, concrheu3, concsta2, concstat, concthro, concthyr, concthyr2, concvit2, concvita, Contralateral_surgery, conwhen, corticos,
cortihorm2, creat, crp_all, CRP_avg, CRP_dif, crp_source, CRP_var, CST3_pg_ug, CST3_serum_luminex, CTGF, cTNI_plasma, CTSA, CTSB, CTSL1, CTSS, cyr61,
date_ic_patient, date_ic_researcher, Date.of.birth, date.previous.operation, date1yr, date3mon, dateapprox_latest, dateapprox_worst, dateapprox1, dateapprox2,
dateapprox3, dateapprox4, dateend1, dateend2, dateend3, dateend4, dateend5, dateend6, dateexact_latest, dateexact_worst, dateexact1, dateexact2, dateexact3,
dateexact4, dateok, dermacor, diastoli, diet801, diet802, diet803, diet804, diet805, diet806, diet807, diet808, diet809, diet810, diet811, diet812, diet813,
diet814, diet815, diet816, diet817, diet818, diet819, diet820, diet821, diet822, diet823, diet824, dipyridi, diuretic, diuretic2, diuretic3, DM, DM.composite,
duaalantiplatelet, duplend, eaindexl, eaindexr, eCigarettes, edaplaqu_recalc, edavrspl, EGR, EMMPRIN_45kD, EMMPRIN_58kD, ENDOGLIN, endpoint1, endpoint2, endpoint3,
endpoint4, endpoint5, endpoint6, Eotaxin1, EP_CAD, ep_cad_t_30days, ep_cad_t_3years, EP_CAD_time, ep_cad.30days, EP_CI, ep_ci_t_30days, ep_ci_t_3years, EP_CI_time,
ep_com_t_30days, ep_com_t_3years, EP_composite, EP_composite_time, EP_coronary, ep_coronary_t_3years, EP_coronary_time, EP_CVdeath, ep_cvdeath_t_30days,
ep_cvdeath_t_3years, EP_CVdeath_time, EP_death, ep_death_t_30days, ep_death_t_3years, EP_death_time, EP_fatalCVA, ep_fatalCVA_t_30days, ep_fatalCVA_t_3years,
EP_fatalCVA_time, EP_hemorrhagic_stroke, ep_hemorrhagic_stroke_t_3years, EP_hemorrhagic_stroke_time, ep_hemorrhagic_stroke.3years, EP_ischemic_stroke,
ep_ischemic_stroke_t_3years, EP_ischemic_stroke_time, ep_ischemic_stroke.3years, EP_leg_amputation, EP_leg_amputation_time, ep_legamputation_t_30days,
ep_legamputation_t_3years, EP_major, ep_major_t_30days, ep_major_t_3years, EP_major_time, EP_MI, ep_mi_t_30days, ep_mi_t_3years, EP_MI_time, EP_nonstroke_event,
EP_nonstroke_event_time, ep_nonstroke_t_3years, EP_peripheral, ep_peripheral_t_30days, ep_peripheral_t_3years, EP_peripheral_time, EP_pta, ep_pta_t_30days,
ep_pta_t_3years, EP_pta_time, EP_stroke, ep_stroke_t_30days, ep_stroke_t_3years, EP_stroke_time, EP_strokeCVdeath, ep_strokeCVdeath_t_30days,
ep_strokeCVdeath_t_3years, EP_strokeCVdeath_time, EP_strokedeath, ep_strokedeath_t_30days, ep_strokedeath_t_3years, EP_strokedeath_time, ePackYearsSmoking,
epcad.3years, epci.30days, epci.3years, epcom.30days, epcom.3years, epcoronary.3years, epcvdeath.30days, epcvdeath.3years, epdeath.30days, epdeath.3years,
epfatalCVA.30days, epfatalCVA.3years, eplegamputation.30days, eplegamputation.3years, epmajor.30days, epmajor.3years, epmi.30days, epmi.3years, epnonstroke.3years,
epperipheral.30days, epperipheral.3years, eppta.30days, eppta.3years, epstroke.30days, epstroke.3years, epstrokeCVdeath.30days, epstrokeCVdeath.3years,
epstrokedeath.30days, epstrokedeath.3years, erec, Estradiol, everstroke_composite, Everstroke_Ipsilateral, exer901, exer902, exer903, exer904, exer905, exer906,
exer9071, exer9072, exer9073, exer9074, exer9075, exer9076, exer908, exer909, exer910, eyedrop, EZis, FABP_serum, FABP4, FABP4_pg_ug, FABP4_serum_luminex, fat,
Fat.bin_10, Fat.bin_40, Femoral.interv, FH_AAA_broth, FH_AAA_comp, FH_AAA_mat, FH_AAA_parent, FH_AAA_pat, FH_AAA_sibling, FH_AAA_sis, FH_amp_broth, FH_amp_comp,
FH_amp_mat, FH_amp_parent, FH_amp_pat, FH_amp_sibling, FH_amp_sis, FH_CAD_broth, FH_CAD_comp, FH_CAD_mat, FH_CAD_parent, FH_CAD_pat, FH_CAD_sibling, FH_CAD_sis,
FH_corcalc_broth, FH_corcalc_comp, FH_corcalc_mat, FH_corcalc_parent, FH_corcalc_pat, FH_corcalc_sibling, FH_corcalc_sis, FH_CVD_broth, FH_CVD_comp, FH_CVD_mat,
FH_CVD_parent, FH_CVD_pat, FH_CVD_sibling, FH_CVD_sis, FH_CVdeath_broth, FH_CVdeath_comp, FH_CVdeath_mat, FH_CVdeath_parent, FH_CVdeath_pat, FH_CVdeath_sibling,
FH_CVdeath_sis, FH_DM_broth, FH_DM_comp, FH_DM_mat, FH_DM_parent, FH_DM_pat, FH_DM_sibling, FH_DM_sis, FH_HC_broth, FH_HC_comp, FH_HC_mat, FH_HC_parent, FH_HC_pat,
FH_HC_sibling, FH_HC_sis, FH_HT_broth, FH_HT_comp, FH_HT_mat, FH_HT_parent, FH_HT_pat, FH_HT_sibling, FH_HT_sis, FH_MI_broth, FH_MI_comp, FH_MI_mat, FH_MI_parent,
FH_MI_pat, FH_MI_sibling, FH_MI_sis, FH_otherCVD_broth, FH_otherCVD_comp, FH_otherCVD_mat, FH_otherCVD_parent, FH_otherCVD_pat, FH_otherCVD_sibling,
FH_otherCVD_sis, FH_PAD_broth, FH_PAD_comp, FH_PAD_mat, FH_PAD_parent, FH_PAD_pat, FH_PAD_sibling, FH_PAD_sis, FH_PAV_broth, FH_PAV_comp, FH_PAV_mat, FH_PAV_parent,
FH_PAV_pat, FH_PAV_sibling, FH_PAV_sis, FH_POB_broth, FH_POB_comp, FH_POB_mat, FH_POB_parent, FH_POB_pat, FH_POB_sibling, FH_POB_sis, FH_risk_broth, FH_risk_comp,
FH_risk_mat, FH_risk_parent, FH_risk_pat, FH_risk_sibling, FH_risk_sis, FH_Stroke_broth, FH_Stroke_comp, FH_Stroke_mat, FH_Stroke_parent, FH_Stroke_pat,
FH_Stroke_sibling, FH_Stroke_sis, FH_tromb_broth, FH_tromb_comp, FH_tromb_mat, FH_tromb_parent, FH_tromb_pat, FH_tromb_sibling, FH_tromb_sis, filter_$, folicaci,
followup1, followup2, followup3, Fontaine, FU_check, FU_check_date, FU.cutt.off.30days, FU.cutt.off.3years, FU1JAAR, FU2JAAR, FU3JAAR, FURIN_low, FURIN_up,
GDF15_plasma, geen_med, Gender, GFR_CG, GFR_MDRD, glucose, GR_Segment, GrB_plaque, GrB_serum, grel, GrK_plaque, GrK_serum, GrM_plaque, GrM_serum, HA, hb, HDAC9,
HDL, HDL_2016, HDL_all, HDL_avg, HDL_clinic, HDL_dif, HDL_final, HDL_finalCU, hdl_source, HDL_var, heart300, heart301, heart302, heart303, heart304, heart305,
heart306, heart307, heart308, heart309, heart310, heart311, heart312, heart313, heart314, heart315, heart316, heart317, heart318, heart319, heart320, heart321,
heart322, heart323, heart324, heart325, heart326, heart327, heart328, HIF1A, ho1, homocys, Hospital, hrt31301, hsCRP_plasma, ht, HYAL55KD, HYALURON,
Hypertension.composite, Hypertension.drugs, Hypertension1, Hypertension2, IL1_Beta, IL10, IL12, IL13, IL17, IL2, IL21, IL4, IL5, IL6, IL6_pg_ug_2015,
IL6R_pg_ug_2015, IL8, IL8_pg_ug_2015, IL9, indexsymptoms_latest, indexsymptoms_latest_4g, indexsymptoms_worst, indexsymptoms_worst_4g, INFG, informedconsent,
insulin, insuline, INVULDAT, IP10, IPH_extended.bin, IPH.bin, ironfoli, ironfoli2, KDOQI, latest, LDL, LDL_2016, LDL_all, LDL_avg, LDL_clinic, LDL_dif, LDL_final,
LDL_finalCU, ldl_source, LDL_var, leg501, leg502, leg503, leg504, leg505, leg506, leg507, leg508, leg509, leg510, leg511, leg512, leg513, leg514, leg515, leg516,
leg517, leg518, leg519, leg520, LMW1STME, LTB4, LTB4R, macmean0, macrophages, macrophages_location, Macrophages.bin, MAP, Mast_cells_plaque, max.followup, MCP1,
MCP1_pg_ug_2015, MCSF_pg_ug_2015, MDC, Med_notes, Med.ablock, Med.ACE_inh, Med.acetylsal, Med.acetylsal_Combi1, Med.acetylsal_Combi2, Med.acetylsal_Combi3,
Med.ADPinh, Med.all.antiplatelet, Med.angiot2.antag, Med.antiarrh, Med.anticoagulants, Med.ascal, Med.aspirin.derived, Med.bblocker, Med.calc_antag,
Med.dipyridamole, Med.diuretic, Med.LLD, Med.nitrate, Med.otheranthyp, Med.renin, Med.statin, Med.statin.derived, Med.Statin.LLD, Med.statin2, media, MG_H1, MI_Dx,
MI_Dx1, MI_Dx2, MIF, MIG, MIP1a, miRNA100_RNU19, miRNA100_RNU48, miRNA155_RNU19, miRNA155_RNU48, MMP14, MMP2, MMP2TIMP2, MMP8, MMP9, MMP9TIMP1, MPO_plasma, MRP_14,
MRP_8, MRP_8_14C, MRP_8_14C_buhlmann, MRP14_plasma, MRP8_14C_plasma, MRP8_plasma, negatibl, neuropsy, neuropsy2, neuropsy3, neuropsy4, neurpsy5, neutrophils, NGAL,
NGAL_low, NGAL_MMP9_complex, NGAL_MMP9_local, NGAL_MMP9_peripheral, NGAL_total, NGAL_up, nitrate, nitrate2, NOD1, NOD2, nogobt1_recalculated, NTproBNP_plasma,
Number_Events_Sorter, Number_Sorted_CD14, Number_Sorted_CD20, Number_Sorted_CD4_Cells, Number_Sorted_CD8_Cells, oac701, oac702, oac70305, oac704, oac705, oac706,
oac707, oac708, oac709, oac710, oac711, oac712, oac713, oac714, OKyear, OPG, OPG_plasma, OPN, OPN_2013, OPN_plasma, OR_blood, Oral.glucose.inh, oralgluc, oralgluc2,
oralgluc3, oralgluc4, ORyear, othanthyp, othcoron, other, other2, OverallPlaquePhenotype, PAI1_pg_ug_2015, PAOD, PARC, patch, PCSK9_plasma, PDGF_BB_plasma,
Percentage_CD14, Percentage_CD20, Percentage_CD4, Percentage_CD8, Peripheral.interv, PKC, PLA2_plasma, plaquephenotype, positibl, PrimaryLast, PrimaryLast1,
prostagl, PulsePressure, qual01, qual02, qual0301, qual0302, qual0303, qual0304, qual0305, qual0306, qual0307, qual0308, qual0309, qual0310, qual0401, qual0402,
qual0403, qual0404, qual0501, qual0502, qual0503, qual06, qual07, qual08, qual0901, qual0902, qual0903, qual0904, qual0905, qual0906, qual0907, qual0908, qual0909,
qual1010, qual1101, qual1102, qual1103, qual1104, RAAS_med, RANTES, RANTES_pg_ug_2015, RANTES_plasma, Ras, RE50_01, RE70_01, Renine_recode, renineinh, restenos,
restenosisOK, rheuma, rheuma2, rheuma3, risk601, risk602, risk603, risk604, risk605, risk606, risk607, risk608, risk609, risk610, risk611, risk612, risk613,
risk614, risk615, risk616, risk617, risk618, risk619, risk620, SHBG, sICAM1, SMAD1_5_8, SMAD2, SMAD3, smc, smc_location, smc_macrophages_ratio, SMC.bin, smcmean0,
SmokerCurrent, SmokingReported, SmokingYearOR, stat3P, statin2, statines, ste3mext, sten1yr, sten3mo, stenose, stenosis_con_bin, Stenosis_contralateral,
Stenosis_ipsilateral, Stroke_Dx, Stroke_eitherside, Stroke_history, Stroke_Symptoms, StrokeTIA_Dx, StrokeTIA_history, StrokeTIA_Symptoms, STUDY_NUMBER, sympt,
Sympt_latest, Sympt_worst, sympt1, sympt2, sympt3, sympt4, Symptoms.3g, Symptoms.4g, Symptoms.5G, systolic, T_NUMBER, TARC, TAT_plasma, TC_2016, TC_all, TC_avg,
TC_clinic, TC_dif, TC_final, TC_finalCU, TC_var, Testosterone, TG_2016, TG_all, TG_avg, TG_clinic, TG_dif, TG_final, TG_finalCU, TG_var, TGF, TGFB, thrombos,
thrombus, thrombus_location, thrombus_new, thrombus_organization, thrombus_organization_v2, thrombus_percentage, thyros2, thyrosta, Time_event_OR, TimeOR_latest,
TimeOR_latest_4g, TimeOR_worst, TimeOR_worst_4g, TIMP1, TIMP2, TISNOW, TNFA, totalchol, totalcholesterol_source, tractdig, tractdig2, tractdig3, tractdig4,
tractres, Treatment.DM, TREM1, triglyceride_source, triglyceriden, Trop1, Trop1DT, Trop2, Trop2DT, Trop3, Trop3DT, TropmaxpostOK, TropoMax, TropoMaxDT,
tropomaxpositief, TSratio_blood, TSratio_plaque, UPID, validation_date, validation1, validation2, validation3, validation4, validation5, validation6, VAR00001,
VEFGA, VEGFA_plasma, vegfa422, vessel_density, vessel_density_additional, vessel_density_averaged, vessel_density_Timo2012, vessel_density_Timo2012_2,
vessel_density_Timo2013, vitamin, vitamin2, vitb12, VRAGENLIJST, vWF_plasma, WBC_THAW, Which.femoral.artery, Whichoperation, writtenIC, yearablo, yearablo2,
yearablo3, yearace, yearace2, yearacet, yearanal, yearanal2, yearanal3, yearangi, yearanta, yearanta2, yearanti, yearanti2, yearbblo, yearbblo2, yearcalc,
yearcalc2, yearcalreg, yearcarb, yearchol, yearchol2, yearchol3, yearclau1, yearclau2, yearclop, yearcom1, yearcom2, yearcom3, yearcort, yearcorthorm2, yearderm,
yeardig, yeardig2, yeardig3, yeardig4, yeardipy, yeardiur, yeardiur2, yeardiur3, yearerec, yeareye, yeargluc, yeargluc2, yeargluc3, yeargluc4, yeargrel, yearinsu,
yeariron, yeariron2, yearneur, yearneur2, yearneur3, yearneur4, yearnitr, yearnitr2, yearOR_bin_2010, YearOR_per2years, yearotant, yearotcor, yearoth2, yearothe,
yearpros, yearpsy5, yearren, yearresp, yearrheu, yearrheu2, yearrheu3, yearsta2, yearstat, yearthro, yearthyr, yearthyr2, yearvit2, yearvita, Yrs.no.smoking,
Yrs.smoking
The following objects are masked from AEDB (pos = 5):
ABI_70, ABI_max, ABI_mean, ABI_min, ABI_OP, ablock, ablock2, ablock3, aceinhib, aceinhib2, acetylsa, Adiponectin_pg_ug_2015, AE_AAA_bijzonderheden, Age, Age_Q,
AgeSQR, aid, Aldosteron_recode, alg10201, alg10202, alg10203, alg10204, alg10205, alg105, alg106, alg109, alg110, alg113, alg114, alg115, ALOX5, analg2, analg3,
analgeti, Ang2, angioii, ANGPT2, anti_apoA1_IgG, anti_apoA1_index, anti_apoA1_na, antiall, antiall2, antiarrh, antiarrh2, ANXA2, AP_Dx, AP_Dx1, AP_Dx2, APOB,
artercon, Artery_summary, arteryop, AsymptSympt, AsymptSympt2G, bblock, bblock2, blocko, blocksnr, BMI, BMI_US, BMI_WHO, BMI30ormore, brain401, brain402, brain403,
brain404, brain405, brain406, brain407, brain408, brain409, brain410, brain411, brain412, brain413, brn40701, bspoed, CAD_Dx, CAD_Dx1, CAD_Dx2, CAD_history,
CADPAOD_history, Calc.bin, calcification, calcium, calcium2, calreg, carbasal, cardioembolic, Caspase3_7, CAV1, CD44, CD44V3, CEA_or_CAS, CEL, CFD_recalc, cholverl,
cholverl2, cholverl3, CI_history, clau1, clau2, Claudication, clopidog, CML, collagen, Collagen.bin, combi1, combi2, combi3, comorbidity.DM, concablo, concablo2,
concablo3, concace2, concacei, concacet, concalle, concanal, concanal2, concanal3, concangi, concanta2, concanti, concanti2, concbblo, concbblo2, conccalc,
conccalc2, conccalreg, conccarb, concchol, concchol2, concchol3, concclau1, concclau2, concclop, conccom1, conccom2, conccom3, conccort, conccorthorm2, concderm,
concdig, concdig2, concdig3, concdig4, concdipy, concdiur, concdiur2, concdiur3, concerec, conceye, concgluc, concgluc2, concgluc3, concgluc4, concgrel, concinsu,
conciron, conciron2, concneur, concneur2, concneur3, concneur4, concnitr, concnitr2, concotant, concotcor, concoth2, concothe, concpros, concpsy5, concren,
concresp, concrheu, concrheu2, concrheu3, concsta2, concstat, concthro, concthyr, concthyr2, concvit2, concvita, Contralateral_surgery, conwhen, corticos,
cortihorm2, creat, crp_all, CRP_avg, CRP_dif, crp_source, CRP_var, CST3_pg_ug, CST3_serum_luminex, CTGF, cTNI_plasma, CTSA, CTSB, CTSL1, CTSS, cyr61,
date_ic_patient, date_ic_researcher, Date.of.birth, date.previous.operation, date1yr, date3mon, dateapprox_latest, dateapprox_worst, dateapprox1, dateapprox2,
dateapprox3, dateapprox4, dateend1, dateend2, dateend3, dateend4, dateend5, dateend6, dateexact_latest, dateexact_worst, dateexact1, dateexact2, dateexact3,
dateexact4, dateok, dermacor, diastoli, diet801, diet802, diet803, diet804, diet805, diet806, diet807, diet808, diet809, diet810, diet811, diet812, diet813,
diet814, diet815, diet816, diet817, diet818, diet819, diet820, diet821, diet822, diet823, diet824, dipyridi, diuretic, diuretic2, diuretic3, DM, DM.composite,
duaalantiplatelet, duplend, eaindexl, eaindexr, eCigarettes, edaplaqu_recalc, edavrspl, EGR, EMMPRIN_45kD, EMMPRIN_58kD, ENDOGLIN, endpoint1, endpoint2, endpoint3,
endpoint4, endpoint5, endpoint6, Eotaxin1, EP_CAD, ep_cad_t_30days, ep_cad_t_3years, EP_CAD_time, ep_cad.30days, EP_CI, ep_ci_t_30days, ep_ci_t_3years, EP_CI_time,
ep_com_t_30days, ep_com_t_3years, EP_composite, EP_composite_time, EP_coronary, ep_coronary_t_3years, EP_coronary_time, EP_CVdeath, ep_cvdeath_t_30days,
ep_cvdeath_t_3years, EP_CVdeath_time, EP_death, ep_death_t_30days, ep_death_t_3years, EP_death_time, EP_fatalCVA, ep_fatalCVA_t_30days, ep_fatalCVA_t_3years,
EP_fatalCVA_time, EP_hemorrhagic_stroke, ep_hemorrhagic_stroke_t_3years, EP_hemorrhagic_stroke_time, ep_hemorrhagic_stroke.3years, EP_ischemic_stroke,
ep_ischemic_stroke_t_3years, EP_ischemic_stroke_time, ep_ischemic_stroke.3years, EP_leg_amputation, EP_leg_amputation_time, ep_legamputation_t_30days,
ep_legamputation_t_3years, EP_major, ep_major_t_30days, ep_major_t_3years, EP_major_time, EP_MI, ep_mi_t_30days, ep_mi_t_3years, EP_MI_time, EP_nonstroke_event,
EP_nonstroke_event_time, ep_nonstroke_t_3years, EP_peripheral, ep_peripheral_t_30days, ep_peripheral_t_3years, EP_peripheral_time, EP_pta, ep_pta_t_30days,
ep_pta_t_3years, EP_pta_time, EP_stroke, ep_stroke_t_30days, ep_stroke_t_3years, EP_stroke_time, EP_strokeCVdeath, ep_strokeCVdeath_t_30days,
ep_strokeCVdeath_t_3years, EP_strokeCVdeath_time, EP_strokedeath, ep_strokedeath_t_30days, ep_strokedeath_t_3years, EP_strokedeath_time, ePackYearsSmoking,
epcad.3years, epci.30days, epci.3years, epcom.30days, epcom.3years, epcoronary.3years, epcvdeath.30days, epcvdeath.3years, epdeath.30days, epdeath.3years,
epfatalCVA.30days, epfatalCVA.3years, eplegamputation.30days, eplegamputation.3years, epmajor.30days, epmajor.3years, epmi.30days, epmi.3years, epnonstroke.3years,
epperipheral.30days, epperipheral.3years, eppta.30days, eppta.3years, epstroke.30days, epstroke.3years, epstrokeCVdeath.30days, epstrokeCVdeath.3years,
epstrokedeath.30days, epstrokedeath.3years, erec, Estradiol, everstroke_composite, Everstroke_Ipsilateral, exer901, exer902, exer903, exer904, exer905, exer906,
exer9071, exer9072, exer9073, exer9074, exer9075, exer9076, exer908, exer909, exer910, eyedrop, EZis, FABP_serum, FABP4, FABP4_pg_ug, FABP4_serum_luminex, fat,
Fat.bin_10, Fat.bin_40, Femoral.interv, FH_AAA_broth, FH_AAA_comp, FH_AAA_mat, FH_AAA_parent, FH_AAA_pat, FH_AAA_sibling, FH_AAA_sis, FH_amp_broth, FH_amp_comp,
FH_amp_mat, FH_amp_parent, FH_amp_pat, FH_amp_sibling, FH_amp_sis, FH_CAD_broth, FH_CAD_comp, FH_CAD_mat, FH_CAD_parent, FH_CAD_pat, FH_CAD_sibling, FH_CAD_sis,
FH_corcalc_broth, FH_corcalc_comp, FH_corcalc_mat, FH_corcalc_parent, FH_corcalc_pat, FH_corcalc_sibling, FH_corcalc_sis, FH_CVD_broth, FH_CVD_comp, FH_CVD_mat,
FH_CVD_parent, FH_CVD_pat, FH_CVD_sibling, FH_CVD_sis, FH_CVdeath_broth, FH_CVdeath_comp, FH_CVdeath_mat, FH_CVdeath_parent, FH_CVdeath_pat, FH_CVdeath_sibling,
FH_CVdeath_sis, FH_DM_broth, FH_DM_comp, FH_DM_mat, FH_DM_parent, FH_DM_pat, FH_DM_sibling, FH_DM_sis, FH_HC_broth, FH_HC_comp, FH_HC_mat, FH_HC_parent, FH_HC_pat,
FH_HC_sibling, FH_HC_sis, FH_HT_broth, FH_HT_comp, FH_HT_mat, FH_HT_parent, FH_HT_pat, FH_HT_sibling, FH_HT_sis, FH_MI_broth, FH_MI_comp, FH_MI_mat, FH_MI_parent,
FH_MI_pat, FH_MI_sibling, FH_MI_sis, FH_otherCVD_broth, FH_otherCVD_comp, FH_otherCVD_mat, FH_otherCVD_parent, FH_otherCVD_pat, FH_otherCVD_sibling,
FH_otherCVD_sis, FH_PAD_broth, FH_PAD_comp, FH_PAD_mat, FH_PAD_parent, FH_PAD_pat, FH_PAD_sibling, FH_PAD_sis, FH_PAV_broth, FH_PAV_comp, FH_PAV_mat, FH_PAV_parent,
FH_PAV_pat, FH_PAV_sibling, FH_PAV_sis, FH_POB_broth, FH_POB_comp, FH_POB_mat, FH_POB_parent, FH_POB_pat, FH_POB_sibling, FH_POB_sis, FH_risk_broth, FH_risk_comp,
FH_risk_mat, FH_risk_parent, FH_risk_pat, FH_risk_sibling, FH_risk_sis, FH_Stroke_broth, FH_Stroke_comp, FH_Stroke_mat, FH_Stroke_parent, FH_Stroke_pat,
FH_Stroke_sibling, FH_Stroke_sis, FH_tromb_broth, FH_tromb_comp, FH_tromb_mat, FH_tromb_parent, FH_tromb_pat, FH_tromb_sibling, FH_tromb_sis, filter_$, folicaci,
followup1, followup2, followup3, Fontaine, FU_check, FU_check_date, FU.cutt.off.30days, FU.cutt.off.3years, FU1JAAR, FU2JAAR, FU3JAAR, FURIN_low, FURIN_up,
GDF15_plasma, geen_med, Gender, GFR_CG, GFR_MDRD, glucose, GR_Segment, GrB_plaque, GrB_serum, grel, GrK_plaque, GrK_serum, GrM_plaque, GrM_serum, HA, hb, HDAC9,
HDL, HDL_2016, HDL_all, HDL_avg, HDL_clinic, HDL_dif, HDL_final, HDL_finalCU, hdl_source, HDL_var, heart300, heart301, heart302, heart303, heart304, heart305,
heart306, heart307, heart308, heart309, heart310, heart311, heart312, heart313, heart314, heart315, heart316, heart317, heart318, heart319, heart320, heart321,
heart322, heart323, heart324, heart325, heart326, heart327, heart328, HIF1A, ho1, homocys, Hospital, hrt31301, hsCRP_plasma, ht, HYAL55KD, HYALURON,
Hypertension.composite, Hypertension.drugs, Hypertension1, Hypertension2, IL1_Beta, IL10, IL12, IL13, IL17, IL2, IL21, IL4, IL5, IL6, IL6_pg_ug_2015,
IL6R_pg_ug_2015, IL8, IL8_pg_ug_2015, IL9, indexsymptoms_latest, indexsymptoms_latest_4g, indexsymptoms_worst, indexsymptoms_worst_4g, INFG, informedconsent,
insulin, insuline, INVULDAT, IP10, IPH_extended.bin, IPH.bin, ironfoli, ironfoli2, KDOQI, latest, LDL, LDL_2016, LDL_all, LDL_avg, LDL_clinic, LDL_dif, LDL_final,
LDL_finalCU, ldl_source, LDL_var, leg501, leg502, leg503, leg504, leg505, leg506, leg507, leg508, leg509, leg510, leg511, leg512, leg513, leg514, leg515, leg516,
leg517, leg518, leg519, leg520, LMW1STME, LTB4, LTB4R, macmean0, macrophages, macrophages_location, Macrophages.bin, MAP, Mast_cells_plaque, max.followup, MCP1,
MCP1_pg_ug_2015, MCSF_pg_ug_2015, MDC, Med_notes, Med.ablock, Med.ACE_inh, Med.acetylsal, Med.acetylsal_Combi1, Med.acetylsal_Combi2, Med.acetylsal_Combi3,
Med.ADPinh, Med.all.antiplatelet, Med.angiot2.antag, Med.antiarrh, Med.anticoagulants, Med.ascal, Med.aspirin.derived, Med.bblocker, Med.calc_antag,
Med.dipyridamole, Med.diuretic, Med.LLD, Med.nitrate, Med.otheranthyp, Med.renin, Med.statin, Med.statin.derived, Med.Statin.LLD, Med.statin2, media, MG_H1, MI_Dx,
MI_Dx1, MI_Dx2, MIF, MIG, MIP1a, miRNA100_RNU19, miRNA100_RNU48, miRNA155_RNU19, miRNA155_RNU48, MMP14, MMP2, MMP2TIMP2, MMP8, MMP9, MMP9TIMP1, MPO_plasma, MRP_14,
MRP_8, MRP_8_14C, MRP_8_14C_buhlmann, MRP14_plasma, MRP8_14C_plasma, MRP8_plasma, negatibl, neuropsy, neuropsy2, neuropsy3, neuropsy4, neurpsy5, neutrophils, NGAL,
NGAL_low, NGAL_MMP9_complex, NGAL_MMP9_local, NGAL_MMP9_peripheral, NGAL_total, NGAL_up, nitrate, nitrate2, NOD1, NOD2, nogobt1_recalculated, NTproBNP_plasma,
Number_Events_Sorter, Number_Sorted_CD14, Number_Sorted_CD20, Number_Sorted_CD4_Cells, Number_Sorted_CD8_Cells, oac701, oac702, oac70305, oac704, oac705, oac706,
oac707, oac708, oac709, oac710, oac711, oac712, oac713, oac714, OKyear, OPG, OPG_plasma, OPN, OPN_2013, OPN_plasma, OR_blood, Oral.glucose.inh, oralgluc, oralgluc2,
oralgluc3, oralgluc4, ORyear, othanthyp, othcoron, other, other2, OverallPlaquePhenotype, PAI1_pg_ug_2015, PAOD, PARC, patch, PCSK9_plasma, PDGF_BB_plasma,
Percentage_CD14, Percentage_CD20, Percentage_CD4, Percentage_CD8, Peripheral.interv, PKC, PLA2_plasma, plaquephenotype, positibl, PrimaryLast, PrimaryLast1,
prostagl, PulsePressure, qual01, qual02, qual0301, qual0302, qual0303, qual0304, qual0305, qual0306, qual0307, qual0308, qual0309, qual0310, qual0401, qual0402,
qual0403, qual0404, qual0501, qual0502, qual0503, qual06, qual07, qual08, qual0901, qual0902, qual0903, qual0904, qual0905, qual0906, qual0907, qual0908, qual0909,
qual1010, qual1101, qual1102, qual1103, qual1104, RAAS_med, RANTES, RANTES_pg_ug_2015, RANTES_plasma, Ras, RE50_01, RE70_01, Renine_recode, renineinh, restenos,
restenosisOK, rheuma, rheuma2, rheuma3, risk601, risk602, risk603, risk604, risk605, risk606, risk607, risk608, risk609, risk610, risk611, risk612, risk613,
risk614, risk615, risk616, risk617, risk618, risk619, risk620, SHBG, sICAM1, SMAD1_5_8, SMAD2, SMAD3, smc, smc_location, smc_macrophages_ratio, SMC.bin, smcmean0,
SmokerCurrent, SmokingReported, SmokingYearOR, stat3P, statin2, statines, ste3mext, sten1yr, sten3mo, stenose, stenosis_con_bin, Stenosis_contralateral,
Stenosis_ipsilateral, Stroke_Dx, Stroke_eitherside, Stroke_history, Stroke_Symptoms, StrokeTIA_Dx, StrokeTIA_history, StrokeTIA_Symptoms, STUDY_NUMBER, sympt,
Sympt_latest, Sympt_worst, sympt1, sympt2, sympt3, sympt4, Symptoms.3g, Symptoms.4g, Symptoms.5G, systolic, T_NUMBER, TARC, TAT_plasma, TC_2016, TC_all, TC_avg,
TC_clinic, TC_dif, TC_final, TC_finalCU, TC_var, Testosterone, TG_2016, TG_all, TG_avg, TG_clinic, TG_dif, TG_final, TG_finalCU, TG_var, TGF, TGFB, thrombos,
thrombus, thrombus_location, thrombus_new, thrombus_organization, thrombus_organization_v2, thrombus_percentage, thyros2, thyrosta, Time_event_OR, TimeOR_latest,
TimeOR_latest_4g, TimeOR_worst, TimeOR_worst_4g, TIMP1, TIMP2, TISNOW, TNFA, totalchol, totalcholesterol_source, tractdig, tractdig2, tractdig3, tractdig4,
tractres, Treatment.DM, TREM1, triglyceride_source, triglyceriden, Trop1, Trop1DT, Trop2, Trop2DT, Trop3, Trop3DT, TropmaxpostOK, TropoMax, TropoMaxDT,
tropomaxpositief, TSratio_blood, TSratio_plaque, UPID, validation_date, validation1, validation2, validation3, validation4, validation5, validation6, VAR00001,
VEFGA, VEGFA_plasma, vegfa422, vessel_density, vessel_density_additional, vessel_density_averaged, vessel_density_Timo2012, vessel_density_Timo2012_2,
vessel_density_Timo2013, vitamin, vitamin2, vitb12, VRAGENLIJST, vWF_plasma, WBC_THAW, Which.femoral.artery, Whichoperation, writtenIC, yearablo, yearablo2,
yearablo3, yearace, yearace2, yearacet, yearanal, yearanal2, yearanal3, yearangi, yearanta, yearanta2, yearanti, yearanti2, yearbblo, yearbblo2, yearcalc,
yearcalc2, yearcalreg, yearcarb, yearchol, yearchol2, yearchol3, yearclau1, yearclau2, yearclop, yearcom1, yearcom2, yearcom3, yearcort, yearcorthorm2, yearderm,
yeardig, yeardig2, yeardig3, yeardig4, yeardipy, yeardiur, yeardiur2, yeardiur3, yearerec, yeareye, yeargluc, yeargluc2, yeargluc3, yeargluc4, yeargrel, yearinsu,
yeariron, yeariron2, yearneur, yearneur2, yearneur3, yearneur4, yearnitr, yearnitr2, yearOR_bin_2010, YearOR_per2years, yearotant, yearotcor, yearoth2, yearothe,
yearpros, yearpsy5, yearren, yearresp, yearrheu, yearrheu2, yearrheu3, yearsta2, yearstat, yearthro, yearthyr, yearthyr2, yearvit2, yearvita, Yrs.no.smoking,
Yrs.smoking
The following objects are masked from AEDB (pos = 6):
ABI_70, ABI_max, ABI_mean, ABI_min, ABI_OP, ablock, ablock2, ablock3, aceinhib, aceinhib2, acetylsa, Adiponectin_pg_ug_2015, AE_AAA_bijzonderheden, Age, Age_Q,
AgeSQR, aid, Aldosteron_recode, alg10201, alg10202, alg10203, alg10204, alg10205, alg105, alg106, alg109, alg110, alg113, alg114, alg115, ALOX5, analg2, analg3,
analgeti, Ang2, angioii, ANGPT2, anti_apoA1_IgG, anti_apoA1_index, anti_apoA1_na, antiall, antiall2, antiarrh, antiarrh2, ANXA2, AP_Dx, AP_Dx1, AP_Dx2, APOB,
artercon, Artery_summary, arteryop, AsymptSympt, AsymptSympt2G, bblock, bblock2, blocko, blocksnr, BMI, BMI_US, BMI_WHO, BMI30ormore, brain401, brain402, brain403,
brain404, brain405, brain406, brain407, brain408, brain409, brain410, brain411, brain412, brain413, brn40701, bspoed, CAD_Dx, CAD_Dx1, CAD_Dx2, CAD_history,
CADPAOD_history, Calc.bin, calcification, calcium, calcium2, calreg, carbasal, cardioembolic, Caspase3_7, CAV1, CD44, CD44V3, CEA_or_CAS, CEL, CFD_recalc, cholverl,
cholverl2, cholverl3, CI_history, clau1, clau2, Claudication, clopidog, CML, collagen, Collagen.bin, combi1, combi2, combi3, comorbidity.DM, concablo, concablo2,
concablo3, concace2, concacei, concacet, concalle, concanal, concanal2, concanal3, concangi, concanta2, concanti, concanti2, concbblo, concbblo2, conccalc,
conccalc2, conccalreg, conccarb, concchol, concchol2, concchol3, concclau1, concclau2, concclop, conccom1, conccom2, conccom3, conccort, conccorthorm2, concderm,
concdig, concdig2, concdig3, concdig4, concdipy, concdiur, concdiur2, concdiur3, concerec, conceye, concgluc, concgluc2, concgluc3, concgluc4, concgrel, concinsu,
conciron, conciron2, concneur, concneur2, concneur3, concneur4, concnitr, concnitr2, concotant, concotcor, concoth2, concothe, concpros, concpsy5, concren,
concresp, concrheu, concrheu2, concrheu3, concsta2, concstat, concthro, concthyr, concthyr2, concvit2, concvita, Contralateral_surgery, conwhen, corticos,
cortihorm2, creat, crp_all, CRP_avg, CRP_dif, crp_source, CRP_var, CST3_pg_ug, CST3_serum_luminex, CTGF, cTNI_plasma, CTSA, CTSB, CTSL1, CTSS, cyr61,
date_ic_patient, date_ic_researcher, Date.of.birth, date.previous.operation, date1yr, date3mon, dateapprox_latest, dateapprox_worst, dateapprox1, dateapprox2,
dateapprox3, dateapprox4, dateend1, dateend2, dateend3, dateend4, dateend5, dateend6, dateexact_latest, dateexact_worst, dateexact1, dateexact2, dateexact3,
dateexact4, dateok, dermacor, diastoli, diet801, diet802, diet803, diet804, diet805, diet806, diet807, diet808, diet809, diet810, diet811, diet812, diet813,
diet814, diet815, diet816, diet817, diet818, diet819, diet820, diet821, diet822, diet823, diet824, dipyridi, diuretic, diuretic2, diuretic3, DM, DM.composite,
duaalantiplatelet, duplend, eaindexl, eaindexr, eCigarettes, edaplaqu_recalc, edavrspl, EGR, EMMPRIN_45kD, EMMPRIN_58kD, ENDOGLIN, endpoint1, endpoint2, endpoint3,
endpoint4, endpoint5, endpoint6, Eotaxin1, EP_CAD, ep_cad_t_30days, ep_cad_t_3years, EP_CAD_time, ep_cad.30days, EP_CI, ep_ci_t_30days, ep_ci_t_3years, EP_CI_time,
ep_com_t_30days, ep_com_t_3years, EP_composite, EP_composite_time, EP_coronary, ep_coronary_t_3years, EP_coronary_time, EP_CVdeath, ep_cvdeath_t_30days,
ep_cvdeath_t_3years, EP_CVdeath_time, EP_death, ep_death_t_30days, ep_death_t_3years, EP_death_time, EP_fatalCVA, ep_fatalCVA_t_30days, ep_fatalCVA_t_3years,
EP_fatalCVA_time, EP_hemorrhagic_stroke, ep_hemorrhagic_stroke_t_3years, EP_hemorrhagic_stroke_time, ep_hemorrhagic_stroke.3years, EP_ischemic_stroke,
ep_ischemic_stroke_t_3years, EP_ischemic_stroke_time, ep_ischemic_stroke.3years, EP_leg_amputation, EP_leg_amputation_time, ep_legamputation_t_30days,
ep_legamputation_t_3years, EP_major, ep_major_t_30days, ep_major_t_3years, EP_major_time, EP_MI, ep_mi_t_30days, ep_mi_t_3years, EP_MI_time, EP_nonstroke_event,
EP_nonstroke_event_time, ep_nonstroke_t_3years, EP_peripheral, ep_peripheral_t_30days, ep_peripheral_t_3years, EP_peripheral_time, EP_pta, ep_pta_t_30days,
ep_pta_t_3years, EP_pta_time, EP_stroke, ep_stroke_t_30days, ep_stroke_t_3years, EP_stroke_time, EP_strokeCVdeath, ep_strokeCVdeath_t_30days,
ep_strokeCVdeath_t_3years, EP_strokeCVdeath_time, EP_strokedeath, ep_strokedeath_t_30days, ep_strokedeath_t_3years, EP_strokedeath_time, ePackYearsSmoking,
epcad.3years, epci.30days, epci.3years, epcom.30days, epcom.3years, epcoronary.3years, epcvdeath.30days, epcvdeath.3years, epdeath.30days, epdeath.3years,
epfatalCVA.30days, epfatalCVA.3years, eplegamputation.30days, eplegamputation.3years, epmajor.30days, epmajor.3years, epmi.30days, epmi.3years, epnonstroke.3years,
epperipheral.30days, epperipheral.3years, eppta.30days, eppta.3years, epstroke.30days, epstroke.3years, epstrokeCVdeath.30days, epstrokeCVdeath.3years,
epstrokedeath.30days, epstrokedeath.3years, erec, Estradiol, everstroke_composite, Everstroke_Ipsilateral, exer901, exer902, exer903, exer904, exer905, exer906,
exer9071, exer9072, exer9073, exer9074, exer9075, exer9076, exer908, exer909, exer910, eyedrop, EZis, FABP_serum, FABP4, FABP4_pg_ug, FABP4_serum_luminex, fat,
Fat.bin_10, Fat.bin_40, Femoral.interv, FH_AAA_broth, FH_AAA_comp, FH_AAA_mat, FH_AAA_parent, FH_AAA_pat, FH_AAA_sibling, FH_AAA_sis, FH_amp_broth, FH_amp_comp,
FH_amp_mat, FH_amp_parent, FH_amp_pat, FH_amp_sibling, FH_amp_sis, FH_CAD_broth, FH_CAD_comp, FH_CAD_mat, FH_CAD_parent, FH_CAD_pat, FH_CAD_sibling, FH_CAD_sis,
FH_corcalc_broth, FH_corcalc_comp, FH_corcalc_mat, FH_corcalc_parent, FH_corcalc_pat, FH_corcalc_sibling, FH_corcalc_sis, FH_CVD_broth, FH_CVD_comp, FH_CVD_mat,
FH_CVD_parent, FH_CVD_pat, FH_CVD_sibling, FH_CVD_sis, FH_CVdeath_broth, FH_CVdeath_comp, FH_CVdeath_mat, FH_CVdeath_parent, FH_CVdeath_pat, FH_CVdeath_sibling,
FH_CVdeath_sis, FH_DM_broth, FH_DM_comp, FH_DM_mat, FH_DM_parent, FH_DM_pat, FH_DM_sibling, FH_DM_sis, FH_HC_broth, FH_HC_comp, FH_HC_mat, FH_HC_parent, FH_HC_pat,
FH_HC_sibling, FH_HC_sis, FH_HT_broth, FH_HT_comp, FH_HT_mat, FH_HT_parent, FH_HT_pat, FH_HT_sibling, FH_HT_sis, FH_MI_broth, FH_MI_comp, FH_MI_mat, FH_MI_parent,
FH_MI_pat, FH_MI_sibling, FH_MI_sis, FH_otherCVD_broth, FH_otherCVD_comp, FH_otherCVD_mat, FH_otherCVD_parent, FH_otherCVD_pat, FH_otherCVD_sibling,
FH_otherCVD_sis, FH_PAD_broth, FH_PAD_comp, FH_PAD_mat, FH_PAD_parent, FH_PAD_pat, FH_PAD_sibling, FH_PAD_sis, FH_PAV_broth, FH_PAV_comp, FH_PAV_mat, FH_PAV_parent,
FH_PAV_pat, FH_PAV_sibling, FH_PAV_sis, FH_POB_broth, FH_POB_comp, FH_POB_mat, FH_POB_parent, FH_POB_pat, FH_POB_sibling, FH_POB_sis, FH_risk_broth, FH_risk_comp,
FH_risk_mat, FH_risk_parent, FH_risk_pat, FH_risk_sibling, FH_risk_sis, FH_Stroke_broth, FH_Stroke_comp, FH_Stroke_mat, FH_Stroke_parent, FH_Stroke_pat,
FH_Stroke_sibling, FH_Stroke_sis, FH_tromb_broth, FH_tromb_comp, FH_tromb_mat, FH_tromb_parent, FH_tromb_pat, FH_tromb_sibling, FH_tromb_sis, filter_$, folicaci,
followup1, followup2, followup3, Fontaine, FU_check, FU_check_date, FU.cutt.off.30days, FU.cutt.off.3years, FU1JAAR, FU2JAAR, FU3JAAR, FURIN_low, FURIN_up,
GDF15_plasma, geen_med, Gender, GFR_CG, GFR_MDRD, glucose, GR_Segment, GrB_plaque, GrB_serum, grel, GrK_plaque, GrK_serum, GrM_plaque, GrM_serum, HA, hb, HDAC9,
HDL, HDL_2016, HDL_all, HDL_avg, HDL_clinic, HDL_dif, HDL_final, HDL_finalCU, hdl_source, HDL_var, heart300, heart301, heart302, heart303, heart304, heart305,
heart306, heart307, heart308, heart309, heart310, heart311, heart312, heart313, heart314, heart315, heart316, heart317, heart318, heart319, heart320, heart321,
heart322, heart323, heart324, heart325, heart326, heart327, heart328, HIF1A, ho1, homocys, Hospital, hrt31301, hsCRP_plasma, ht, HYAL55KD, HYALURON,
Hypertension.composite, Hypertension.drugs, Hypertension1, Hypertension2, IL1_Beta, IL10, IL12, IL13, IL17, IL2, IL21, IL4, IL5, IL6, IL6_pg_ug_2015,
IL6R_pg_ug_2015, IL8, IL8_pg_ug_2015, IL9, indexsymptoms_latest, indexsymptoms_latest_4g, indexsymptoms_worst, indexsymptoms_worst_4g, INFG, informedconsent,
insulin, insuline, INVULDAT, IP10, IPH_extended.bin, IPH.bin, ironfoli, ironfoli2, KDOQI, latest, LDL, LDL_2016, LDL_all, LDL_avg, LDL_clinic, LDL_dif, LDL_final,
LDL_finalCU, ldl_source, LDL_var, leg501, leg502, leg503, leg504, leg505, leg506, leg507, leg508, leg509, leg510, leg511, leg512, leg513, leg514, leg515, leg516,
leg517, leg518, leg519, leg520, LMW1STME, LTB4, LTB4R, macmean0, macrophages, macrophages_location, Macrophages.bin, MAP, Mast_cells_plaque, max.followup, MCP1,
MCP1_pg_ug_2015, MCSF_pg_ug_2015, MDC, Med_notes, Med.ablock, Med.ACE_inh, Med.acetylsal, Med.acetylsal_Combi1, Med.acetylsal_Combi2, Med.acetylsal_Combi3,
Med.ADPinh, Med.all.antiplatelet, Med.angiot2.antag, Med.antiarrh, Med.anticoagulants, Med.ascal, Med.aspirin.derived, Med.bblocker, Med.calc_antag,
Med.dipyridamole, Med.diuretic, Med.LLD, Med.nitrate, Med.otheranthyp, Med.renin, Med.statin, Med.statin.derived, Med.Statin.LLD, Med.statin2, media, MG_H1, MI_Dx,
MI_Dx1, MI_Dx2, MIF, MIG, MIP1a, miRNA100_RNU19, miRNA100_RNU48, miRNA155_RNU19, miRNA155_RNU48, MMP14, MMP2, MMP2TIMP2, MMP8, MMP9, MMP9TIMP1, MPO_plasma, MRP_14,
MRP_8, MRP_8_14C, MRP_8_14C_buhlmann, MRP14_plasma, MRP8_14C_plasma, MRP8_plasma, negatibl, neuropsy, neuropsy2, neuropsy3, neuropsy4, neurpsy5, neutrophils, NGAL,
NGAL_low, NGAL_MMP9_complex, NGAL_MMP9_local, NGAL_MMP9_peripheral, NGAL_total, NGAL_up, nitrate, nitrate2, NOD1, NOD2, nogobt1_recalculated, NTproBNP_plasma,
Number_Events_Sorter, Number_Sorted_CD14, Number_Sorted_CD20, Number_Sorted_CD4_Cells, Number_Sorted_CD8_Cells, oac701, oac702, oac70305, oac704, oac705, oac706,
oac707, oac708, oac709, oac710, oac711, oac712, oac713, oac714, OKyear, OPG, OPG_plasma, OPN, OPN_2013, OPN_plasma, OR_blood, Oral.glucose.inh, oralgluc, oralgluc2,
oralgluc3, oralgluc4, ORyear, othanthyp, othcoron, other, other2, OverallPlaquePhenotype, PAI1_pg_ug_2015, PAOD, PARC, patch, PCSK9_plasma, PDGF_BB_plasma,
Percentage_CD14, Percentage_CD20, Percentage_CD4, Percentage_CD8, Peripheral.interv, PKC, PLA2_plasma, plaquephenotype, positibl, PrimaryLast, PrimaryLast1,
prostagl, PulsePressure, qual01, qual02, qual0301, qual0302, qual0303, qual0304, qual0305, qual0306, qual0307, qual0308, qual0309, qual0310, qual0401, qual0402,
qual0403, qual0404, qual0501, qual0502, qual0503, qual06, qual07, qual08, qual0901, qual0902, qual0903, qual0904, qual0905, qual0906, qual0907, qual0908, qual0909,
qual1010, qual1101, qual1102, qual1103, qual1104, RAAS_med, RANTES, RANTES_pg_ug_2015, RANTES_plasma, Ras, RE50_01, RE70_01, Renine_recode, renineinh, restenos,
restenosisOK, rheuma, rheuma2, rheuma3, risk601, risk602, risk603, risk604, risk605, risk606, risk607, risk608, risk609, risk610, risk611, risk612, risk613,
risk614, risk615, risk616, risk617, risk618, risk619, risk620, SHBG, sICAM1, SMAD1_5_8, SMAD2, SMAD3, smc, smc_location, smc_macrophages_ratio, SMC.bin, smcmean0,
SmokerCurrent, SmokingReported, SmokingYearOR, stat3P, statin2, statines, ste3mext, sten1yr, sten3mo, stenose, stenosis_con_bin, Stenosis_contralateral,
Stenosis_ipsilateral, Stroke_Dx, Stroke_eitherside, Stroke_history, Stroke_Symptoms, StrokeTIA_Dx, StrokeTIA_history, StrokeTIA_Symptoms, STUDY_NUMBER, sympt,
Sympt_latest, Sympt_worst, sympt1, sympt2, sympt3, sympt4, Symptoms.3g, Symptoms.4g, Symptoms.5G, systolic, T_NUMBER, TARC, TAT_plasma, TC_2016, TC_all, TC_avg,
TC_clinic, TC_dif, TC_final, TC_finalCU, TC_var, Testosterone, TG_2016, TG_all, TG_avg, TG_clinic, TG_dif, TG_final, TG_finalCU, TG_var, TGF, TGFB, thrombos,
thrombus, thrombus_location, thrombus_new, thrombus_organization, thrombus_organization_v2, thrombus_percentage, thyros2, thyrosta, Time_event_OR, TimeOR_latest,
TimeOR_latest_4g, TimeOR_worst, TimeOR_worst_4g, TIMP1, TIMP2, TISNOW, TNFA, totalchol, totalcholesterol_source, tractdig, tractdig2, tractdig3, tractdig4,
tractres, Treatment.DM, TREM1, triglyceride_source, triglyceriden, Trop1, Trop1DT, Trop2, Trop2DT, Trop3, Trop3DT, TropmaxpostOK, TropoMax, TropoMaxDT,
tropomaxpositief, TSratio_blood, TSratio_plaque, UPID, validation_date, validation1, validation2, validation3, validation4, validation5, validation6, VAR00001,
VEFGA, VEGFA_plasma, vegfa422, vessel_density, vessel_density_additional, vessel_density_averaged, vessel_density_Timo2012, vessel_density_Timo2012_2,
vessel_density_Timo2013, vitamin, vitamin2, vitb12, VRAGENLIJST, vWF_plasma, WBC_THAW, Which.femoral.artery, Whichoperation, writtenIC, yearablo, yearablo2,
yearablo3, yearace, yearace2, yearacet, yearanal, yearanal2, yearanal3, yearangi, yearanta, yearanta2, yearanti, yearanti2, yearbblo, yearbblo2, yearcalc,
yearcalc2, yearcalreg, yearcarb, yearchol, yearchol2, yearchol3, yearclau1, yearclau2, yearclop, yearcom1, yearcom2, yearcom3, yearcort, yearcorthorm2, yearderm,
yeardig, yeardig2, yeardig3, yeardig4, yeardipy, yeardiur, yeardiur2, yeardiur3, yearerec, yeareye, yeargluc, yeargluc2, yeargluc3, yeargluc4, yeargrel, yearinsu,
yeariron, yeariron2, yearneur, yearneur2, yearneur3, yearneur4, yearnitr, yearnitr2, yearOR_bin_2010, YearOR_per2years, yearotant, yearotcor, yearoth2, yearothe,
yearpros, yearpsy5, yearren, yearresp, yearrheu, yearrheu2, yearrheu3, yearsta2, yearstat, yearthro, yearthyr, yearthyr2, yearvit2, yearvita, Yrs.no.smoking,
Yrs.smoking
The following objects are masked from AEDB (pos = 7):
ABI_70, ABI_max, ABI_mean, ABI_min, ABI_OP, ablock, ablock2, ablock3, aceinhib, aceinhib2, acetylsa, Adiponectin_pg_ug_2015, AE_AAA_bijzonderheden, Age, Age_Q,
AgeSQR, aid, Aldosteron_recode, alg10201, alg10202, alg10203, alg10204, alg10205, alg105, alg106, alg109, alg110, alg113, alg114, alg115, ALOX5, analg2, analg3,
analgeti, Ang2, angioii, ANGPT2, anti_apoA1_IgG, anti_apoA1_index, anti_apoA1_na, antiall, antiall2, antiarrh, antiarrh2, ANXA2, AP_Dx, AP_Dx1, AP_Dx2, APOB,
artercon, Artery_summary, arteryop, AsymptSympt, AsymptSympt2G, bblock, bblock2, blocko, blocksnr, BMI, BMI_US, BMI_WHO, BMI30ormore, brain401, brain402, brain403,
brain404, brain405, brain406, brain407, brain408, brain409, brain410, brain411, brain412, brain413, brn40701, bspoed, CAD_Dx, CAD_Dx1, CAD_Dx2, CAD_history,
CADPAOD_history, Calc.bin, calcification, calcium, calcium2, calreg, carbasal, cardioembolic, Caspase3_7, CAV1, CD44, CD44V3, CEA_or_CAS, CEL, CFD_recalc, cholverl,
cholverl2, cholverl3, CI_history, clau1, clau2, Claudication, clopidog, CML, collagen, Collagen.bin, combi1, combi2, combi3, comorbidity.DM, concablo, concablo2,
concablo3, concace2, concacei, concacet, concalle, concanal, concanal2, concanal3, concangi, concanta2, concanti, concanti2, concbblo, concbblo2, conccalc,
conccalc2, conccalreg, conccarb, concchol, concchol2, concchol3, concclau1, concclau2, concclop, conccom1, conccom2, conccom3, conccort, conccorthorm2, concderm,
concdig, concdig2, concdig3, concdig4, concdipy, concdiur, concdiur2, concdiur3, concerec, conceye, concgluc, concgluc2, concgluc3, concgluc4, concgrel, concinsu,
conciron, conciron2, concneur, concneur2, concneur3, concneur4, concnitr, concnitr2, concotant, concotcor, concoth2, concothe, concpros, concpsy5, concren,
concresp, concrheu, concrheu2, concrheu3, concsta2, concstat, concthro, concthyr, concthyr2, concvit2, concvita, Contralateral_surgery, conwhen, corticos,
cortihorm2, creat, crp_all, CRP_avg, CRP_dif, crp_source, CRP_var, CST3_pg_ug, CST3_serum_luminex, CTGF, cTNI_plasma, CTSA, CTSB, CTSL1, CTSS, cyr61,
date_ic_patient, date_ic_researcher, Date.of.birth, date.previous.operation, date1yr, date3mon, dateapprox_latest, dateapprox_worst, dateapprox1, dateapprox2,
dateapprox3, dateapprox4, dateend1, dateend2, dateend3, dateend4, dateend5, dateend6, dateexact_latest, dateexact_worst, dateexact1, dateexact2, dateexact3,
dateexact4, dateok, dermacor, DiabetesStatus, diastoli, diet801, diet802, diet803, diet804, diet805, diet806, diet807, diet808, diet809, diet810, diet811, diet812,
diet813, diet814, diet815, diet816, diet817, diet818, diet819, diet820, diet821, diet822, diet823, diet824, dipyridi, diuretic, diuretic2, diuretic3, DM,
DM.composite, duaalantiplatelet, duplend, eaindexl, eaindexr, eCigarettes, edaplaqu_recalc, edavrspl, EGR, EMMPRIN_45kD, EMMPRIN_58kD, ENDOGLIN, endpoint1,
endpoint2, endpoint3, endpoint4, endpoint5, endpoint6, Eotaxin1, EP_CAD, ep_cad_t_30days, ep_cad_t_3years, EP_CAD_time, ep_cad.30days, EP_CI, ep_ci_t_30days,
ep_ci_t_3years, EP_CI_time, ep_com_t_30days, ep_com_t_3years, EP_composite, EP_composite_time, EP_coronary, ep_coronary_t_3years, EP_coronary_time, EP_CVdeath,
ep_cvdeath_t_30days, ep_cvdeath_t_3years, EP_CVdeath_time, EP_death, ep_death_t_30days, ep_death_t_3years, EP_death_time, EP_fatalCVA, ep_fatalCVA_t_30days,
ep_fatalCVA_t_3years, EP_fatalCVA_time, EP_hemorrhagic_stroke, ep_hemorrhagic_stroke_t_3years, EP_hemorrhagic_stroke_time, ep_hemorrhagic_stroke.3years,
EP_ischemic_stroke, ep_ischemic_stroke_t_3years, EP_ischemic_stroke_time, ep_ischemic_stroke.3years, EP_leg_amputation, EP_leg_amputation_time,
ep_legamputation_t_30days, ep_legamputation_t_3years, EP_major, ep_major_t_30days, ep_major_t_3years, EP_major_time, EP_MI, ep_mi_t_30days, ep_mi_t_3years,
EP_MI_time, EP_nonstroke_event, EP_nonstroke_event_time, ep_nonstroke_t_3years, EP_peripheral, ep_peripheral_t_30days, ep_peripheral_t_3years, EP_peripheral_time,
EP_pta, ep_pta_t_30days, ep_pta_t_3years, EP_pta_time, EP_stroke, ep_stroke_t_30days, ep_stroke_t_3years, EP_stroke_time, EP_strokeCVdeath,
ep_strokeCVdeath_t_30days, ep_strokeCVdeath_t_3years, EP_strokeCVdeath_time, EP_strokedeath, ep_strokedeath_t_30days, ep_strokedeath_t_3years, EP_strokedeath_time,
ePackYearsSmoking, epcad.3years, epci.30days, epci.3years, epcom.30days, epcom.3years, epcoronary.3years, epcvdeath.30days, epcvdeath.3years, epdeath.30days,
epdeath.3years, epfatalCVA.30days, epfatalCVA.3years, eplegamputation.30days, eplegamputation.3years, epmajor.30days, epmajor.3years, epmi.30days, epmi.3years,
epnonstroke.3years, epperipheral.30days, epperipheral.3years, eppta.30days, eppta.3years, epstroke.30days, epstroke.3years, epstrokeCVdeath.30days,
epstrokeCVdeath.3years, epstrokedeath.30days, epstrokedeath.3years, erec, Estradiol, everstroke_composite, Everstroke_Ipsilateral, exer901, exer902, exer903,
exer904, exer905, exer906, exer9071, exer9072, exer9073, exer9074, exer9075, exer9076, exer908, exer909, exer910, eyedrop, EZis, FABP_serum, FABP4, FABP4_pg_ug,
FABP4_serum_luminex, fat, Fat.bin_10, Fat.bin_40, Femoral.interv, FH_AAA_broth, FH_AAA_comp, FH_AAA_mat, FH_AAA_parent, FH_AAA_pat, FH_AAA_sibling, FH_AAA_sis,
FH_amp_broth, FH_amp_comp, FH_amp_mat, FH_amp_parent, FH_amp_pat, FH_amp_sibling, FH_amp_sis, FH_CAD_broth, FH_CAD_comp, FH_CAD_mat, FH_CAD_parent, FH_CAD_pat,
FH_CAD_sibling, FH_CAD_sis, FH_corcalc_broth, FH_corcalc_comp, FH_corcalc_mat, FH_corcalc_parent, FH_corcalc_pat, FH_corcalc_sibling, FH_corcalc_sis, FH_CVD_broth,
FH_CVD_comp, FH_CVD_mat, FH_CVD_parent, FH_CVD_pat, FH_CVD_sibling, FH_CVD_sis, FH_CVdeath_broth, FH_CVdeath_comp, FH_CVdeath_mat, FH_CVdeath_parent,
FH_CVdeath_pat, FH_CVdeath_sibling, FH_CVdeath_sis, FH_DM_broth, FH_DM_comp, FH_DM_mat, FH_DM_parent, FH_DM_pat, FH_DM_sibling, FH_DM_sis, FH_HC_broth, FH_HC_comp,
FH_HC_mat, FH_HC_parent, FH_HC_pat, FH_HC_sibling, FH_HC_sis, FH_HT_broth, FH_HT_comp, FH_HT_mat, FH_HT_parent, FH_HT_pat, FH_HT_sibling, FH_HT_sis, FH_MI_broth,
FH_MI_comp, FH_MI_mat, FH_MI_parent, FH_MI_pat, FH_MI_sibling, FH_MI_sis, FH_otherCVD_broth, FH_otherCVD_comp, FH_otherCVD_mat, FH_otherCVD_parent, FH_otherCVD_pat,
FH_otherCVD_sibling, FH_otherCVD_sis, FH_PAD_broth, FH_PAD_comp, FH_PAD_mat, FH_PAD_parent, FH_PAD_pat, FH_PAD_sibling, FH_PAD_sis, FH_PAV_broth, FH_PAV_comp,
FH_PAV_mat, FH_PAV_parent, FH_PAV_pat, FH_PAV_sibling, FH_PAV_sis, FH_POB_broth, FH_POB_comp, FH_POB_mat, FH_POB_parent, FH_POB_pat, FH_POB_sibling, FH_POB_sis,
FH_risk_broth, FH_risk_comp, FH_risk_mat, FH_risk_parent, FH_risk_pat, FH_risk_sibling, FH_risk_sis, FH_Stroke_broth, FH_Stroke_comp, FH_Stroke_mat,
FH_Stroke_parent, FH_Stroke_pat, FH_Stroke_sibling, FH_Stroke_sis, FH_tromb_broth, FH_tromb_comp, FH_tromb_mat, FH_tromb_parent, FH_tromb_pat, FH_tromb_sibling,
FH_tromb_sis, filter_$, folicaci, followup1, followup2, followup3, Fontaine, FU_check, FU_check_date, FU.cutt.off.30days, FU.cutt.off.3years, FU1JAAR, FU2JAAR,
FU3JAAR, FURIN_low, FURIN_up, GDF15_plasma, geen_med, Gender, GFR_CG, GFR_MDRD, glucose, GR_Segment, GrB_plaque, GrB_serum, grel, GrK_plaque, GrK_serum, GrM_plaque,
GrM_serum, HA, hb, HDAC9, HDL, HDL_2016, HDL_all, HDL_avg, HDL_clinic, HDL_dif, HDL_final, HDL_finalCU, hdl_source, HDL_var, heart300, heart301, heart302, heart303,
heart304, heart305, heart306, heart307, heart308, heart309, heart310, heart311, heart312, heart313, heart314, heart315, heart316, heart317, heart318, heart319,
heart320, heart321, heart322, heart323, heart324, heart325, heart326, heart327, heart328, HIF1A, ho1, homocys, Hospital, hrt31301, hsCRP_plasma, ht, HYAL55KD,
HYALURON, Hypertension.composite, Hypertension.drugs, Hypertension1, Hypertension2, IL1_Beta, IL10, IL12, IL13, IL17, IL2, IL21, IL4, IL5, IL6, IL6_pg_ug_2015,
IL6R_pg_ug_2015, IL8, IL8_pg_ug_2015, IL9, indexsymptoms_latest, indexsymptoms_latest_4g, indexsymptoms_worst, indexsymptoms_worst_4g, INFG, informedconsent,
insulin, insuline, INVULDAT, IP10, IPH_extended.bin, IPH.bin, ironfoli, ironfoli2, KDOQI, latest, LDL, LDL_2016, LDL_all, LDL_avg, LDL_clinic, LDL_dif, LDL_final,
LDL_finalCU, ldl_source, LDL_var, leg501, leg502, leg503, leg504, leg505, leg506, leg507, leg508, leg509, leg510, leg511, leg512, leg513, leg514, leg515, leg516,
leg517, leg518, leg519, leg520, LMW1STME, LTB4, LTB4R, macmean0, macrophages, macrophages_location, Macrophages.bin, MAP, Mast_cells_plaque, max.followup, MCP1,
MCP1_pg_ug_2015, MCSF_pg_ug_2015, MDC, Med_notes, Med.ablock, Med.ACE_inh, Med.acetylsal, Med.acetylsal_Combi1, Med.acetylsal_Combi2, Med.acetylsal_Combi3,
Med.ADPinh, Med.all.antiplatelet, Med.angiot2.antag, Med.antiarrh, Med.anticoagulants, Med.ascal, Med.aspirin.derived, Med.bblocker, Med.calc_antag,
Med.dipyridamole, Med.diuretic, Med.LLD, Med.nitrate, Med.otheranthyp, Med.renin, Med.statin, Med.statin.derived, Med.Statin.LLD, Med.statin2, media, MG_H1, MI_Dx,
MI_Dx1, MI_Dx2, MIF, MIG, MIP1a, miRNA100_RNU19, miRNA100_RNU48, miRNA155_RNU19, miRNA155_RNU48, MMP14, MMP2, MMP2TIMP2, MMP8, MMP9, MMP9TIMP1, MPO_plasma, MRP_14,
MRP_8, MRP_8_14C, MRP_8_14C_buhlmann, MRP14_plasma, MRP8_14C_plasma, MRP8_plasma, negatibl, neuropsy, neuropsy2, neuropsy3, neuropsy4, neurpsy5, neutrophils, NGAL,
NGAL_low, NGAL_MMP9_complex, NGAL_MMP9_local, NGAL_MMP9_peripheral, NGAL_total, NGAL_up, nitrate, nitrate2, NOD1, NOD2, nogobt1_recalculated, NTproBNP_plasma,
Number_Events_Sorter, Number_Sorted_CD14, Number_Sorted_CD20, Number_Sorted_CD4_Cells, Number_Sorted_CD8_Cells, oac701, oac702, oac70305, oac704, oac705, oac706,
oac707, oac708, oac709, oac710, oac711, oac712, oac713, oac714, OKyear, OPG, OPG_plasma, OPN, OPN_2013, OPN_plasma, OR_blood, Oral.glucose.inh, oralgluc, oralgluc2,
oralgluc3, oralgluc4, ORyear, othanthyp, othcoron, other, other2, OverallPlaquePhenotype, PAI1_pg_ug_2015, PAOD, PARC, patch, PCSK9_plasma, PDGF_BB_plasma,
Percentage_CD14, Percentage_CD20, Percentage_CD4, Percentage_CD8, Peripheral.interv, PKC, PLA2_plasma, plaquephenotype, positibl, PrimaryLast, PrimaryLast1,
prostagl, PulsePressure, qual01, qual02, qual0301, qual0302, qual0303, qual0304, qual0305, qual0306, qual0307, qual0308, qual0309, qual0310, qual0401, qual0402,
qual0403, qual0404, qual0501, qual0502, qual0503, qual06, qual07, qual08, qual0901, qual0902, qual0903, qual0904, qual0905, qual0906, qual0907, qual0908, qual0909,
qual1010, qual1101, qual1102, qual1103, qual1104, RAAS_med, RANTES, RANTES_pg_ug_2015, RANTES_plasma, Ras, RE50_01, RE70_01, Renine_recode, renineinh, restenos,
restenosisOK, rheuma, rheuma2, rheuma3, risk601, risk602, risk603, risk604, risk605, risk606, risk607, risk608, risk609, risk610, risk611, risk612, risk613,
risk614, risk615, risk616, risk617, risk618, risk619, risk620, SHBG, sICAM1, SMAD1_5_8, SMAD2, SMAD3, smc, smc_location, smc_macrophages_ratio, SMC.bin, smcmean0,
SmokerCurrent, SmokingReported, SmokingYearOR, stat3P, statin2, statines, ste3mext, sten1yr, sten3mo, stenose, stenosis_con_bin, Stenosis_contralateral,
Stenosis_ipsilateral, Stroke_Dx, Stroke_eitherside, Stroke_history, Stroke_Symptoms, StrokeTIA_Dx, StrokeTIA_history, StrokeTIA_Symptoms, STUDY_NUMBER, sympt,
Sympt_latest, Sympt_worst, sympt1, sympt2, sympt3, sympt4, Symptoms.3g, Symptoms.4g, Symptoms.5G, systolic, T_NUMBER, TARC, TAT_plasma, TC_2016, TC_all, TC_avg,
TC_clinic, TC_dif, TC_final, TC_finalCU, TC_var, Testosterone, TG_2016, TG_all, TG_avg, TG_clinic, TG_dif, TG_final, TG_finalCU, TG_var, TGF, TGFB, thrombos,
thrombus, thrombus_location, thrombus_new, thrombus_organization, thrombus_organization_v2, thrombus_percentage, thyros2, thyrosta, Time_event_OR, TimeOR_latest,
TimeOR_latest_4g, TimeOR_worst, TimeOR_worst_4g, TIMP1, TIMP2, TISNOW, TNFA, totalchol, totalcholesterol_source, tractdig, tractdig2, tractdig3, tractdig4,
tractres, Treatment.DM, TREM1, triglyceride_source, triglyceriden, Trop1, Trop1DT, Trop2, Trop2DT, Trop3, Trop3DT, TropmaxpostOK, TropoMax, TropoMaxDT,
tropomaxpositief, TSratio_blood, TSratio_plaque, UPID, validation_date, validation1, validation2, validation3, validation4, validation5, validation6, VAR00001,
VEFGA, VEGFA_plasma, vegfa422, vessel_density, vessel_density_additional, vessel_density_averaged, vessel_density_Timo2012, vessel_density_Timo2012_2,
vessel_density_Timo2013, vitamin, vitamin2, vitb12, VRAGENLIJST, vWF_plasma, WBC_THAW, Which.femoral.artery, Whichoperation, writtenIC, yearablo, yearablo2,
yearablo3, yearace, yearace2, yearacet, yearanal, yearanal2, yearanal3, yearangi, yearanta, yearanta2, yearanti, yearanti2, yearbblo, yearbblo2, yearcalc,
yearcalc2, yearcalreg, yearcarb, yearchol, yearchol2, yearchol3, yearclau1, yearclau2, yearclop, yearcom1, yearcom2, yearcom3, yearcort, yearcorthorm2, yearderm,
yeardig, yeardig2, yeardig3, yeardig4, yeardipy, yeardiur, yeardiur2, yeardiur3, yearerec, yeareye, yeargluc, yeargluc2, yeargluc3, yeargluc4, yeargrel, yearinsu,
yeariron, yeariron2, yearneur, yearneur2, yearneur3, yearneur4, yearnitr, yearnitr2, yearOR_bin_2010, YearOR_per2years, yearotant, yearotcor, yearoth2, yearothe,
yearpros, yearpsy5, yearren, yearresp, yearrheu, yearrheu2, yearrheu3, yearsta2, yearstat, yearthro, yearthyr, yearthyr2, yearvit2, yearvita, Yrs.no.smoking,
Yrs.smoking
The following objects are masked from AEDB (pos = 8):
ABI_70, ABI_max, ABI_mean, ABI_min, ABI_OP, ablock, ablock2, ablock3, aceinhib, aceinhib2, acetylsa, Adiponectin_pg_ug_2015, AE_AAA_bijzonderheden, Age, Age_Q,
AgeSQR, aid, Aldosteron_recode, alg10201, alg10202, alg10203, alg10204, alg10205, alg105, alg106, alg109, alg110, alg113, alg114, alg115, ALOX5, analg2, analg3,
analgeti, Ang2, angioii, ANGPT2, anti_apoA1_IgG, anti_apoA1_index, anti_apoA1_na, antiall, antiall2, antiarrh, antiarrh2, ANXA2, AP_Dx, AP_Dx1, AP_Dx2, APOB,
artercon, Artery_summary, arteryop, AsymptSympt, AsymptSympt2G, bblock, bblock2, blocko, blocksnr, BMI, BMI_US, BMI_WHO, BMI30ormore, brain401, brain402, brain403,
brain404, brain405, brain406, brain407, brain408, brain409, brain410, brain411, brain412, brain413, brn40701, bspoed, CAD_Dx, CAD_Dx1, CAD_Dx2, CAD_history,
CADPAOD_history, Calc.bin, calcification, calcium, calcium2, calreg, carbasal, cardioembolic, Caspase3_7, CAV1, CD44, CD44V3, CEA_or_CAS, CEL, CFD_recalc, cholverl,
cholverl2, cholverl3, CI_history, clau1, clau2, Claudication, clopidog, CML, collagen, Collagen.bin, combi1, combi2, combi3, comorbidity.DM, concablo, concablo2,
concablo3, concace2, concacei, concacet, concalle, concanal, concanal2, concanal3, concangi, concanta2, concanti, concanti2, concbblo, concbblo2, conccalc,
conccalc2, conccalreg, conccarb, concchol, concchol2, concchol3, concclau1, concclau2, concclop, conccom1, conccom2, conccom3, conccort, conccorthorm2, concderm,
concdig, concdig2, concdig3, concdig4, concdipy, concdiur, concdiur2, concdiur3, concerec, conceye, concgluc, concgluc2, concgluc3, concgluc4, concgrel, concinsu,
conciron, conciron2, concneur, concneur2, concneur3, concneur4, concnitr, concnitr2, concotant, concotcor, concoth2, concothe, concpros, concpsy5, concren,
concresp, concrheu, concrheu2, concrheu3, concsta2, concstat, concthro, concthyr, concthyr2, concvit2, concvita, Contralateral_surgery, conwhen, corticos,
cortihorm2, creat, crp_all, CRP_avg, CRP_dif, crp_source, CRP_var, CST3_pg_ug, CST3_serum_luminex, CTGF, cTNI_plasma, CTSA, CTSB, CTSL1, CTSS, cyr61,
date_ic_patient, date_ic_researcher, Date.of.birth, date.previous.operation, date1yr, date3mon, dateapprox_latest, dateapprox_worst, dateapprox1, dateapprox2,
dateapprox3, dateapprox4, dateend1, dateend2, dateend3, dateend4, dateend5, dateend6, dateexact_latest, dateexact_worst, dateexact1, dateexact2, dateexact3,
dateexact4, dateok, dermacor, diastoli, diet801, diet802, diet803, diet804, diet805, diet806, diet807, diet808, diet809, diet810, diet811, diet812, diet813,
diet814, diet815, diet816, diet817, diet818, diet819, diet820, diet821, diet822, diet823, diet824, dipyridi, diuretic, diuretic2, diuretic3, DM, DM.composite,
duaalantiplatelet, duplend, eaindexl, eaindexr, eCigarettes, edaplaqu_recalc, edavrspl, EGR, EMMPRIN_45kD, EMMPRIN_58kD, ENDOGLIN, endpoint1, endpoint2, endpoint3,
endpoint4, endpoint5, endpoint6, Eotaxin1, EP_CAD, ep_cad_t_30days, ep_cad_t_3years, EP_CAD_time, ep_cad.30days, EP_CI, ep_ci_t_30days, ep_ci_t_3years, EP_CI_time,
ep_com_t_30days, ep_com_t_3years, EP_composite, EP_composite_time, EP_coronary, ep_coronary_t_3years, EP_coronary_time, EP_CVdeath, ep_cvdeath_t_30days,
ep_cvdeath_t_3years, EP_CVdeath_time, EP_death, ep_death_t_30days, ep_death_t_3years, EP_death_time, EP_fatalCVA, ep_fatalCVA_t_30days, ep_fatalCVA_t_3years,
EP_fatalCVA_time, EP_hemorrhagic_stroke, ep_hemorrhagic_stroke_t_3years, EP_hemorrhagic_stroke_time, ep_hemorrhagic_stroke.3years, EP_ischemic_stroke,
ep_ischemic_stroke_t_3years, EP_ischemic_stroke_time, ep_ischemic_stroke.3years, EP_leg_amputation, EP_leg_amputation_time, ep_legamputation_t_30days,
ep_legamputation_t_3years, EP_major, ep_major_t_30days, ep_major_t_3years, EP_major_time, EP_MI, ep_mi_t_30days, ep_mi_t_3years, EP_MI_time, EP_nonstroke_event,
EP_nonstroke_event_time, ep_nonstroke_t_3years, EP_peripheral, ep_peripheral_t_30days, ep_peripheral_t_3years, EP_peripheral_time, EP_pta, ep_pta_t_30days,
ep_pta_t_3years, EP_pta_time, EP_stroke, ep_stroke_t_30days, ep_stroke_t_3years, EP_stroke_time, EP_strokeCVdeath, ep_strokeCVdeath_t_30days,
ep_strokeCVdeath_t_3years, EP_strokeCVdeath_time, EP_strokedeath, ep_strokedeath_t_30days, ep_strokedeath_t_3years, EP_strokedeath_time, ePackYearsSmoking,
epcad.3years, epci.30days, epci.3years, epcom.30days, epcom.3years, epcoronary.3years, epcvdeath.30days, epcvdeath.3years, epdeath.30days, epdeath.3years,
epfatalCVA.30days, epfatalCVA.3years, eplegamputation.30days, eplegamputation.3years, epmajor.30days, epmajor.3years, epmi.30days, epmi.3years, epnonstroke.3years,
epperipheral.30days, epperipheral.3years, eppta.30days, eppta.3years, epstroke.30days, epstroke.3years, epstrokeCVdeath.30days, epstrokeCVdeath.3years,
epstrokedeath.30days, epstrokedeath.3years, erec, Estradiol, everstroke_composite, Everstroke_Ipsilateral, exer901, exer902, exer903, exer904, exer905, exer906,
exer9071, exer9072, exer9073, exer9074, exer9075, exer9076, exer908, exer909, exer910, eyedrop, EZis, FABP_serum, FABP4, FABP4_pg_ug, FABP4_serum_luminex, fat,
Fat.bin_10, Fat.bin_40, Femoral.interv, FH_AAA_broth, FH_AAA_comp, FH_AAA_mat, FH_AAA_parent, FH_AAA_pat, FH_AAA_sibling, FH_AAA_sis, FH_amp_broth, FH_amp_comp,
FH_amp_mat, FH_amp_parent, FH_amp_pat, FH_amp_sibling, FH_amp_sis, FH_CAD_broth, FH_CAD_comp, FH_CAD_mat, FH_CAD_parent, FH_CAD_pat, FH_CAD_sibling, FH_CAD_sis,
FH_corcalc_broth, FH_corcalc_comp, FH_corcalc_mat, FH_corcalc_parent, FH_corcalc_pat, FH_corcalc_sibling, FH_corcalc_sis, FH_CVD_broth, FH_CVD_comp, FH_CVD_mat,
FH_CVD_parent, FH_CVD_pat, FH_CVD_sibling, FH_CVD_sis, FH_CVdeath_broth, FH_CVdeath_comp, FH_CVdeath_mat, FH_CVdeath_parent, FH_CVdeath_pat, FH_CVdeath_sibling,
FH_CVdeath_sis, FH_DM_broth, FH_DM_comp, FH_DM_mat, FH_DM_parent, FH_DM_pat, FH_DM_sibling, FH_DM_sis, FH_HC_broth, FH_HC_comp, FH_HC_mat, FH_HC_parent, FH_HC_pat,
FH_HC_sibling, FH_HC_sis, FH_HT_broth, FH_HT_comp, FH_HT_mat, FH_HT_parent, FH_HT_pat, FH_HT_sibling, FH_HT_sis, FH_MI_broth, FH_MI_comp, FH_MI_mat, FH_MI_parent,
FH_MI_pat, FH_MI_sibling, FH_MI_sis, FH_otherCVD_broth, FH_otherCVD_comp, FH_otherCVD_mat, FH_otherCVD_parent, FH_otherCVD_pat, FH_otherCVD_sibling,
FH_otherCVD_sis, FH_PAD_broth, FH_PAD_comp, FH_PAD_mat, FH_PAD_parent, FH_PAD_pat, FH_PAD_sibling, FH_PAD_sis, FH_PAV_broth, FH_PAV_comp, FH_PAV_mat, FH_PAV_parent,
FH_PAV_pat, FH_PAV_sibling, FH_PAV_sis, FH_POB_broth, FH_POB_comp, FH_POB_mat, FH_POB_parent, FH_POB_pat, FH_POB_sibling, FH_POB_sis, FH_risk_broth, FH_risk_comp,
FH_risk_mat, FH_risk_parent, FH_risk_pat, FH_risk_sibling, FH_risk_sis, FH_Stroke_broth, FH_Stroke_comp, FH_Stroke_mat, FH_Stroke_parent, FH_Stroke_pat,
FH_Stroke_sibling, FH_Stroke_sis, FH_tromb_broth, FH_tromb_comp, FH_tromb_mat, FH_tromb_parent, FH_tromb_pat, FH_tromb_sibling, FH_tromb_sis, filter_$, folicaci,
followup1, followup2, followup3, Fontaine, FU_check, FU_check_date, FU.cutt.off.30days, FU.cutt.off.3years, FU1JAAR, FU2JAAR, FU3JAAR, FURIN_low, FURIN_up,
GDF15_plasma, geen_med, Gender, GFR_CG, GFR_MDRD, glucose, GR_Segment, GrB_plaque, GrB_serum, grel, GrK_plaque, GrK_serum, GrM_plaque, GrM_serum, HA, hb, HDAC9,
HDL, HDL_2016, HDL_all, HDL_avg, HDL_clinic, HDL_dif, HDL_final, HDL_finalCU, hdl_source, HDL_var, heart300, heart301, heart302, heart303, heart304, heart305,
heart306, heart307, heart308, heart309, heart310, heart311, heart312, heart313, heart314, heart315, heart316, heart317, heart318, heart319, heart320, heart321,
heart322, heart323, heart324, heart325, heart326, heart327, heart328, HIF1A, ho1, homocys, Hospital, hrt31301, hsCRP_plasma, ht, HYAL55KD, HYALURON,
Hypertension.composite, Hypertension.drugs, Hypertension1, Hypertension2, IL1_Beta, IL10, IL12, IL13, IL17, IL2, IL21, IL4, IL5, IL6, IL6_pg_ug_2015,
IL6R_pg_ug_2015, IL8, IL8_pg_ug_2015, IL9, indexsymptoms_latest, indexsymptoms_latest_4g, indexsymptoms_worst, indexsymptoms_worst_4g, INFG, informedconsent,
insulin, insuline, INVULDAT, IP10, IPH_extended.bin, IPH.bin, ironfoli, ironfoli2, KDOQI, latest, LDL, LDL_2016, LDL_all, LDL_avg, LDL_clinic, LDL_dif, LDL_final,
LDL_finalCU, ldl_source, LDL_var, leg501, leg502, leg503, leg504, leg505, leg506, leg507, leg508, leg509, leg510, leg511, leg512, leg513, leg514, leg515, leg516,
leg517, leg518, leg519, leg520, LMW1STME, LTB4, LTB4R, macmean0, macrophages, macrophages_location, Macrophages.bin, MAP, Mast_cells_plaque, max.followup, MCP1,
MCP1_pg_ug_2015, MCSF_pg_ug_2015, MDC, Med_notes, Med.ablock, Med.ACE_inh, Med.acetylsal, Med.acetylsal_Combi1, Med.acetylsal_Combi2, Med.acetylsal_Combi3,
Med.ADPinh, Med.all.antiplatelet, Med.angiot2.antag, Med.antiarrh, Med.anticoagulants, Med.ascal, Med.aspirin.derived, Med.bblocker, Med.calc_antag,
Med.dipyridamole, Med.diuretic, Med.LLD, Med.nitrate, Med.otheranthyp, Med.renin, Med.statin, Med.statin.derived, Med.Statin.LLD, Med.statin2, media, MG_H1, MI_Dx,
MI_Dx1, MI_Dx2, MIF, MIG, MIP1a, miRNA100_RNU19, miRNA100_RNU48, miRNA155_RNU19, miRNA155_RNU48, MMP14, MMP2, MMP2TIMP2, MMP8, MMP9, MMP9TIMP1, MPO_plasma, MRP_14,
MRP_8, MRP_8_14C, MRP_8_14C_buhlmann, MRP14_plasma, MRP8_14C_plasma, MRP8_plasma, negatibl, neuropsy, neuropsy2, neuropsy3, neuropsy4, neurpsy5, neutrophils, NGAL,
NGAL_low, NGAL_MMP9_complex, NGAL_MMP9_local, NGAL_MMP9_peripheral, NGAL_total, NGAL_up, nitrate, nitrate2, NOD1, NOD2, nogobt1_recalculated, NTproBNP_plasma,
Number_Events_Sorter, Number_Sorted_CD14, Number_Sorted_CD20, Number_Sorted_CD4_Cells, Number_Sorted_CD8_Cells, oac701, oac702, oac70305, oac704, oac705, oac706,
oac707, oac708, oac709, oac710, oac711, oac712, oac713, oac714, OKyear, OPG, OPG_plasma, OPN, OPN_2013, OPN_plasma, OR_blood, Oral.glucose.inh, oralgluc, oralgluc2,
oralgluc3, oralgluc4, ORyear, othanthyp, othcoron, other, other2, OverallPlaquePhenotype, PAI1_pg_ug_2015, PAOD, PARC, patch, PCSK9_plasma, PDGF_BB_plasma,
Percentage_CD14, Percentage_CD20, Percentage_CD4, Percentage_CD8, Peripheral.interv, PKC, PLA2_plasma, plaquephenotype, positibl, PrimaryLast, PrimaryLast1,
prostagl, PulsePressure, qual01, qual02, qual0301, qual0302, qual0303, qual0304, qual0305, qual0306, qual0307, qual0308, qual0309, qual0310, qual0401, qual0402,
qual0403, qual0404, qual0501, qual0502, qual0503, qual06, qual07, qual08, qual0901, qual0902, qual0903, qual0904, qual0905, qual0906, qual0907, qual0908, qual0909,
qual1010, qual1101, qual1102, qual1103, qual1104, RAAS_med, RANTES, RANTES_pg_ug_2015, RANTES_plasma, Ras, RE50_01, RE70_01, Renine_recode, renineinh, restenos,
restenosisOK, rheuma, rheuma2, rheuma3, risk601, risk602, risk603, risk604, risk605, risk606, risk607, risk608, risk609, risk610, risk611, risk612, risk613,
risk614, risk615, risk616, risk617, risk618, risk619, risk620, SHBG, sICAM1, SMAD1_5_8, SMAD2, SMAD3, smc, smc_location, smc_macrophages_ratio, SMC.bin, smcmean0,
SmokerCurrent, SmokingReported, SmokingYearOR, stat3P, statin2, statines, ste3mext, sten1yr, sten3mo, stenose, stenosis_con_bin, Stenosis_contralateral,
Stenosis_ipsilateral, Stroke_Dx, Stroke_eitherside, Stroke_history, Stroke_Symptoms, StrokeTIA_Dx, StrokeTIA_history, StrokeTIA_Symptoms, STUDY_NUMBER, sympt,
Sympt_latest, Sympt_worst, sympt1, sympt2, sympt3, sympt4, Symptoms.3g, Symptoms.4g, Symptoms.5G, systolic, T_NUMBER, TARC, TAT_plasma, TC_2016, TC_all, TC_avg,
TC_clinic, TC_dif, TC_final, TC_finalCU, TC_var, Testosterone, TG_2016, TG_all, TG_avg, TG_clinic, TG_dif, TG_final, TG_finalCU, TG_var, TGF, TGFB, thrombos,
thrombus, thrombus_location, thrombus_new, thrombus_organization, thrombus_organization_v2, thrombus_percentage, thyros2, thyrosta, Time_event_OR, TimeOR_latest,
TimeOR_latest_4g, TimeOR_worst, TimeOR_worst_4g, TIMP1, TIMP2, TISNOW, TNFA, totalchol, totalcholesterol_source, tractdig, tractdig2, tractdig3, tractdig4,
tractres, Treatment.DM, TREM1, triglyceride_source, triglyceriden, Trop1, Trop1DT, Trop2, Trop2DT, Trop3, Trop3DT, TropmaxpostOK, TropoMax, TropoMaxDT,
tropomaxpositief, TSratio_blood, TSratio_plaque, UPID, validation_date, validation1, validation2, validation3, validation4, validation5, validation6, VAR00001,
VEFGA, VEGFA_plasma, vegfa422, vessel_density, vessel_density_additional, vessel_density_averaged, vessel_density_Timo2012, vessel_density_Timo2012_2,
vessel_density_Timo2013, vitamin, vitamin2, vitb12, VRAGENLIJST, vWF_plasma, WBC_THAW, Which.femoral.artery, Whichoperation, writtenIC, yearablo, yearablo2,
yearablo3, yearace, yearace2, yearacet, yearanal, yearanal2, yearanal3, yearangi, yearanta, yearanta2, yearanti, yearanti2, yearbblo, yearbblo2, yearcalc,
yearcalc2, yearcalreg, yearcarb, yearchol, yearchol2, yearchol3, yearclau1, yearclau2, yearclop, yearcom1, yearcom2, yearcom3, yearcort, yearcorthorm2, yearderm,
yeardig, yeardig2, yeardig3, yeardig4, yeardipy, yeardiur, yeardiur2, yeardiur3, yearerec, yeareye, yeargluc, yeargluc2, yeargluc3, yeargluc4, yeargrel, yearinsu,
yeariron, yeariron2, yearneur, yearneur2, yearneur3, yearneur4, yearnitr, yearnitr2, yearOR_bin_2010, YearOR_per2years, yearotant, yearotcor, yearoth2, yearothe,
yearpros, yearpsy5, yearren, yearresp, yearrheu, yearrheu2, yearrheu3, yearsta2, yearstat, yearthro, yearthyr, yearthyr2, yearvit2, yearvita, Yrs.no.smoking,
Yrs.smokingAEDB[,"epmajor.30days"] <- AEDB$epmajor.3years
AEDB$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0
AEDB[,"epstroke.30days"] <- AEDB$epstroke.3years
AEDB$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0
AEDB[,"epcoronary.30days"] <- AEDB$epcoronary.3years
AEDB$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0
AEDB[,"epcvdeath.30days"] <- AEDB$epcvdeath.3years
AEDB$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0
AEDB[,"epmajor.90days"] <- AEDB$epmajor.3years
AEDB$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0
AEDB[,"epstroke.90days"] <- AEDB$epstroke.3years
AEDB$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0
AEDB[,"epcoronary.90days"] <- AEDB$epcoronary.3years
AEDB$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0
AEDB[,"epcvdeath.90days"] <- AEDB$epcvdeath.3years
AEDB$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0
detach(AEDB)
AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary",
"epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
"epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
"epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
rm(AEDB.temp)
attach(AEDB.CEA)The following objects are masked from AEDB (pos = 3):
ABI_70, ABI_max, ABI_mean, ABI_min, ABI_OP, ablock, ablock2, ablock3, aceinhib, aceinhib2, acetylsa, Adiponectin_pg_ug_2015, AE_AAA_bijzonderheden, Age, Age_Q,
AgeSQR, aid, Aldosteron_recode, alg10201, alg10202, alg10203, alg10204, alg10205, alg105, alg106, alg109, alg110, alg113, alg114, alg115, ALOX5, analg2, analg3,
analgeti, Ang2, angioii, ANGPT2, anti_apoA1_IgG, anti_apoA1_index, anti_apoA1_na, antiall, antiall2, antiarrh, antiarrh2, ANXA2, AP_Dx, AP_Dx1, AP_Dx2, APOB,
artercon, Artery_summary, arteryop, AsymptSympt, AsymptSympt2G, bblock, bblock2, blocko, blocksnr, BMI, BMI_US, BMI_WHO, BMI30ormore, brain401, brain402, brain403,
brain404, brain405, brain406, brain407, brain408, brain409, brain410, brain411, brain412, brain413, brn40701, bspoed, CAD_Dx, CAD_Dx1, CAD_Dx2, CAD_history,
CADPAOD_history, Calc.bin, calcification, calcium, calcium2, calreg, carbasal, cardioembolic, Caspase3_7, CAV1, CD44, CD44V3, CEA_or_CAS, CEL, CFD_recalc, cholverl,
cholverl2, cholverl3, CI_history, clau1, clau2, Claudication, clopidog, CML, collagen, Collagen.bin, combi1, combi2, combi3, comorbidity.DM, concablo, concablo2,
concablo3, concace2, concacei, concacet, concalle, concanal, concanal2, concanal3, concangi, concanta2, concanti, concanti2, concbblo, concbblo2, conccalc,
conccalc2, conccalreg, conccarb, concchol, concchol2, concchol3, concclau1, concclau2, concclop, conccom1, conccom2, conccom3, conccort, conccorthorm2, concderm,
concdig, concdig2, concdig3, concdig4, concdipy, concdiur, concdiur2, concdiur3, concerec, conceye, concgluc, concgluc2, concgluc3, concgluc4, concgrel, concinsu,
conciron, conciron2, concneur, concneur2, concneur3, concneur4, concnitr, concnitr2, concotant, concotcor, concoth2, concothe, concpros, concpsy5, concren,
concresp, concrheu, concrheu2, concrheu3, concsta2, concstat, concthro, concthyr, concthyr2, concvit2, concvita, Contralateral_surgery, conwhen, corticos,
cortihorm2, creat, crp_all, CRP_avg, CRP_dif, crp_source, CRP_var, CST3_pg_ug, CST3_serum_luminex, CTGF, cTNI_plasma, CTSA, CTSB, CTSL1, CTSS, cyr61,
date_ic_patient, date_ic_researcher, Date.of.birth, date.previous.operation, date1yr, date3mon, dateapprox_latest, dateapprox_worst, dateapprox1, dateapprox2,
dateapprox3, dateapprox4, dateend1, dateend2, dateend3, dateend4, dateend5, dateend6, dateexact_latest, dateexact_worst, dateexact1, dateexact2, dateexact3,
dateexact4, dateok, dermacor, DiabetesStatus, diastoli, diet801, diet802, diet803, diet804, diet805, diet806, diet807, diet808, diet809, diet810, diet811, diet812,
diet813, diet814, diet815, diet816, diet817, diet818, diet819, diet820, diet821, diet822, diet823, diet824, dipyridi, diuretic, diuretic2, diuretic3, DM,
DM.composite, duaalantiplatelet, duplend, eaindexl, eaindexr, eCigarettes, edaplaqu_recalc, edavrspl, EGR, EMMPRIN_45kD, EMMPRIN_58kD, ENDOGLIN, endpoint1,
endpoint2, endpoint3, endpoint4, endpoint5, endpoint6, Eotaxin1, EP_CAD, ep_cad_t_30days, ep_cad_t_3years, EP_CAD_time, ep_cad.30days, EP_CI, ep_ci_t_30days,
ep_ci_t_3years, EP_CI_time, ep_com_t_30days, ep_com_t_3years, EP_composite, EP_composite_time, EP_coronary, ep_coronary_t_3years, EP_coronary_time, EP_CVdeath,
ep_cvdeath_t_30days, ep_cvdeath_t_3years, EP_CVdeath_time, EP_death, ep_death_t_30days, ep_death_t_3years, EP_death_time, EP_fatalCVA, ep_fatalCVA_t_30days,
ep_fatalCVA_t_3years, EP_fatalCVA_time, EP_hemorrhagic_stroke, ep_hemorrhagic_stroke_t_3years, EP_hemorrhagic_stroke_time, ep_hemorrhagic_stroke.3years,
EP_ischemic_stroke, ep_ischemic_stroke_t_3years, EP_ischemic_stroke_time, ep_ischemic_stroke.3years, EP_leg_amputation, EP_leg_amputation_time,
ep_legamputation_t_30days, ep_legamputation_t_3years, EP_major, ep_major_t_30days, ep_major_t_3years, EP_major_time, EP_MI, ep_mi_t_30days, ep_mi_t_3years,
EP_MI_time, EP_nonstroke_event, EP_nonstroke_event_time, ep_nonstroke_t_3years, EP_peripheral, ep_peripheral_t_30days, ep_peripheral_t_3years, EP_peripheral_time,
EP_pta, ep_pta_t_30days, ep_pta_t_3years, EP_pta_time, EP_stroke, ep_stroke_t_30days, ep_stroke_t_3years, EP_stroke_time, EP_strokeCVdeath,
ep_strokeCVdeath_t_30days, ep_strokeCVdeath_t_3years, EP_strokeCVdeath_time, EP_strokedeath, ep_strokedeath_t_30days, ep_strokedeath_t_3years, EP_strokedeath_time,
ePackYearsSmoking, epcad.3years, epci.30days, epci.3years, epcom.30days, epcom.3years, epcoronary.3years, epcvdeath.30days, epcvdeath.3years, epdeath.30days,
epdeath.3years, epfatalCVA.30days, epfatalCVA.3years, eplegamputation.30days, eplegamputation.3years, epmajor.30days, epmajor.3years, epmi.30days, epmi.3years,
epnonstroke.3years, epperipheral.30days, epperipheral.3years, eppta.30days, eppta.3years, epstroke.30days, epstroke.3years, epstrokeCVdeath.30days,
epstrokeCVdeath.3years, epstrokedeath.30days, epstrokedeath.3years, erec, Estradiol, everstroke_composite, Everstroke_Ipsilateral, exer901, exer902, exer903,
exer904, exer905, exer906, exer9071, exer9072, exer9073, exer9074, exer9075, exer9076, exer908, exer909, exer910, eyedrop, EZis, FABP_serum, FABP4, FABP4_pg_ug,
FABP4_serum_luminex, fat, Fat.bin_10, Fat.bin_40, Femoral.interv, FH_AAA_broth, FH_AAA_comp, FH_AAA_mat, FH_AAA_parent, FH_AAA_pat, FH_AAA_sibling, FH_AAA_sis,
FH_amp_broth, FH_amp_comp, FH_amp_mat, FH_amp_parent, FH_amp_pat, FH_amp_sibling, FH_amp_sis, FH_CAD_broth, FH_CAD_comp, FH_CAD_mat, FH_CAD_parent, FH_CAD_pat,
FH_CAD_sibling, FH_CAD_sis, FH_corcalc_broth, FH_corcalc_comp, FH_corcalc_mat, FH_corcalc_parent, FH_corcalc_pat, FH_corcalc_sibling, FH_corcalc_sis, FH_CVD_broth,
FH_CVD_comp, FH_CVD_mat, FH_CVD_parent, FH_CVD_pat, FH_CVD_sibling, FH_CVD_sis, FH_CVdeath_broth, FH_CVdeath_comp, FH_CVdeath_mat, FH_CVdeath_parent,
FH_CVdeath_pat, FH_CVdeath_sibling, FH_CVdeath_sis, FH_DM_broth, FH_DM_comp, FH_DM_mat, FH_DM_parent, FH_DM_pat, FH_DM_sibling, FH_DM_sis, FH_HC_broth, FH_HC_comp,
FH_HC_mat, FH_HC_parent, FH_HC_pat, FH_HC_sibling, FH_HC_sis, FH_HT_broth, FH_HT_comp, FH_HT_mat, FH_HT_parent, FH_HT_pat, FH_HT_sibling, FH_HT_sis, FH_MI_broth,
FH_MI_comp, FH_MI_mat, FH_MI_parent, FH_MI_pat, FH_MI_sibling, FH_MI_sis, FH_otherCVD_broth, FH_otherCVD_comp, FH_otherCVD_mat, FH_otherCVD_parent, FH_otherCVD_pat,
FH_otherCVD_sibling, FH_otherCVD_sis, FH_PAD_broth, FH_PAD_comp, FH_PAD_mat, FH_PAD_parent, FH_PAD_pat, FH_PAD_sibling, FH_PAD_sis, FH_PAV_broth, FH_PAV_comp,
FH_PAV_mat, FH_PAV_parent, FH_PAV_pat, FH_PAV_sibling, FH_PAV_sis, FH_POB_broth, FH_POB_comp, FH_POB_mat, FH_POB_parent, FH_POB_pat, FH_POB_sibling, FH_POB_sis,
FH_risk_broth, FH_risk_comp, FH_risk_mat, FH_risk_parent, FH_risk_pat, FH_risk_sibling, FH_risk_sis, FH_Stroke_broth, FH_Stroke_comp, FH_Stroke_mat,
FH_Stroke_parent, FH_Stroke_pat, FH_Stroke_sibling, FH_Stroke_sis, FH_tromb_broth, FH_tromb_comp, FH_tromb_mat, FH_tromb_parent, FH_tromb_pat, FH_tromb_sibling,
FH_tromb_sis, filter_$, folicaci, followup1, followup2, followup3, Fontaine, FU_check, FU_check_date, FU.cutt.off.30days, FU.cutt.off.3years, FU1JAAR, FU2JAAR,
FU3JAAR, FURIN_low, FURIN_up, GDF15_plasma, geen_med, Gender, GFR_CG, GFR_MDRD, glucose, GR_Segment, GrB_plaque, GrB_serum, grel, GrK_plaque, GrK_serum, GrM_plaque,
GrM_serum, HA, hb, HDAC9, HDL, HDL_2016, HDL_all, HDL_avg, HDL_clinic, HDL_dif, HDL_final, HDL_finalCU, hdl_source, HDL_var, heart300, heart301, heart302, heart303,
heart304, heart305, heart306, heart307, heart308, heart309, heart310, heart311, heart312, heart313, heart314, heart315, heart316, heart317, heart318, heart319,
heart320, heart321, heart322, heart323, heart324, heart325, heart326, heart327, heart328, HIF1A, ho1, homocys, Hospital, hrt31301, hsCRP_plasma, ht, HYAL55KD,
HYALURON, Hypertension.composite, Hypertension.drugs, Hypertension1, Hypertension2, IL1_Beta, IL10, IL12, IL13, IL17, IL2, IL21, IL4, IL5, IL6, IL6_pg_ug_2015,
IL6R_pg_ug_2015, IL8, IL8_pg_ug_2015, IL9, indexsymptoms_latest, indexsymptoms_latest_4g, indexsymptoms_worst, indexsymptoms_worst_4g, INFG, informedconsent,
insulin, insuline, INVULDAT, IP10, IPH_extended.bin, IPH.bin, ironfoli, ironfoli2, KDOQI, latest, LDL, LDL_2016, LDL_all, LDL_avg, LDL_clinic, LDL_dif, LDL_final,
LDL_finalCU, ldl_source, LDL_var, leg501, leg502, leg503, leg504, leg505, leg506, leg507, leg508, leg509, leg510, leg511, leg512, leg513, leg514, leg515, leg516,
leg517, leg518, leg519, leg520, LMW1STME, LTB4, LTB4R, macmean0, macrophages, macrophages_location, Macrophages.bin, MAP, Mast_cells_plaque, max.followup, MCP1,
MCP1_pg_ug_2015, MCSF_pg_ug_2015, MDC, Med_notes, Med.ablock, Med.ACE_inh, Med.acetylsal, Med.acetylsal_Combi1, Med.acetylsal_Combi2, Med.acetylsal_Combi3,
Med.ADPinh, Med.all.antiplatelet, Med.angiot2.antag, Med.antiarrh, Med.anticoagulants, Med.ascal, Med.aspirin.derived, Med.bblocker, Med.calc_antag,
Med.dipyridamole, Med.diuretic, Med.LLD, Med.nitrate, Med.otheranthyp, Med.renin, Med.statin, Med.statin.derived, Med.Statin.LLD, Med.statin2, media, MG_H1, MI_Dx,
MI_Dx1, MI_Dx2, MIF, MIG, MIP1a, miRNA100_RNU19, miRNA100_RNU48, miRNA155_RNU19, miRNA155_RNU48, MMP14, MMP2, MMP2TIMP2, MMP8, MMP9, MMP9TIMP1, MPO_plasma, MRP_14,
MRP_8, MRP_8_14C, MRP_8_14C_buhlmann, MRP14_plasma, MRP8_14C_plasma, MRP8_plasma, negatibl, neuropsy, neuropsy2, neuropsy3, neuropsy4, neurpsy5, neutrophils, NGAL,
NGAL_low, NGAL_MMP9_complex, NGAL_MMP9_local, NGAL_MMP9_peripheral, NGAL_total, NGAL_up, nitrate, nitrate2, NOD1, NOD2, nogobt1_recalculated, NTproBNP_plasma,
Number_Events_Sorter, Number_Sorted_CD14, Number_Sorted_CD20, Number_Sorted_CD4_Cells, Number_Sorted_CD8_Cells, oac701, oac702, oac70305, oac704, oac705, oac706,
oac707, oac708, oac709, oac710, oac711, oac712, oac713, oac714, OKyear, OPG, OPG_plasma, OPN, OPN_2013, OPN_plasma, OR_blood, Oral.glucose.inh, oralgluc, oralgluc2,
oralgluc3, oralgluc4, ORyear, othanthyp, othcoron, other, other2, OverallPlaquePhenotype, PAI1_pg_ug_2015, PAOD, PARC, patch, PCSK9_plasma, PDGF_BB_plasma,
Percentage_CD14, Percentage_CD20, Percentage_CD4, Percentage_CD8, Peripheral.interv, PKC, PLA2_plasma, plaquephenotype, positibl, PrimaryLast, PrimaryLast1,
prostagl, PulsePressure, qual01, qual02, qual0301, qual0302, qual0303, qual0304, qual0305, qual0306, qual0307, qual0308, qual0309, qual0310, qual0401, qual0402,
qual0403, qual0404, qual0501, qual0502, qual0503, qual06, qual07, qual08, qual0901, qual0902, qual0903, qual0904, qual0905, qual0906, qual0907, qual0908, qual0909,
qual1010, qual1101, qual1102, qual1103, qual1104, RAAS_med, RANTES, RANTES_pg_ug_2015, RANTES_plasma, Ras, RE50_01, RE70_01, Renine_recode, renineinh, restenos,
restenosisOK, rheuma, rheuma2, rheuma3, risk601, risk602, risk603, risk604, risk605, risk606, risk607, risk608, risk609, risk610, risk611, risk612, risk613,
risk614, risk615, risk616, risk617, risk618, risk619, risk620, SHBG, sICAM1, SMAD1_5_8, SMAD2, SMAD3, smc, smc_location, smc_macrophages_ratio, SMC.bin, smcmean0,
SmokerCurrent, SmokingReported, SmokingYearOR, stat3P, statin2, statines, ste3mext, sten1yr, sten3mo, stenose, stenosis_con_bin, Stenosis_contralateral,
Stenosis_ipsilateral, Stroke_Dx, Stroke_eitherside, Stroke_history, Stroke_Symptoms, StrokeTIA_Dx, StrokeTIA_history, StrokeTIA_Symptoms, STUDY_NUMBER, sympt,
Sympt_latest, Sympt_worst, sympt1, sympt2, sympt3, sympt4, Symptoms.3g, Symptoms.4g, Symptoms.5G, systolic, T_NUMBER, TARC, TAT_plasma, TC_2016, TC_all, TC_avg,
TC_clinic, TC_dif, TC_final, TC_finalCU, TC_var, Testosterone, TG_2016, TG_all, TG_avg, TG_clinic, TG_dif, TG_final, TG_finalCU, TG_var, TGF, TGFB, thrombos,
thrombus, thrombus_location, thrombus_new, thrombus_organization, thrombus_organization_v2, thrombus_percentage, thyros2, thyrosta, Time_event_OR, TimeOR_latest,
TimeOR_latest_4g, TimeOR_worst, TimeOR_worst_4g, TIMP1, TIMP2, TISNOW, TNFA, totalchol, totalcholesterol_source, tractdig, tractdig2, tractdig3, tractdig4,
tractres, Treatment.DM, TREM1, triglyceride_source, triglyceriden, Trop1, Trop1DT, Trop2, Trop2DT, Trop3, Trop3DT, TropmaxpostOK, TropoMax, TropoMaxDT,
tropomaxpositief, TSratio_blood, TSratio_plaque, UPID, validation_date, validation1, validation2, validation3, validation4, validation5, validation6, VAR00001,
VEFGA, VEGFA_plasma, vegfa422, vessel_density, vessel_density_additional, vessel_density_averaged, vessel_density_Timo2012, vessel_density_Timo2012_2,
vessel_density_Timo2013, vitamin, vitamin2, vitb12, VRAGENLIJST, vWF_plasma, WBC_THAW, Which.femoral.artery, Whichoperation, writtenIC, yearablo, yearablo2,
yearablo3, yearace, yearace2, yearacet, yearanal, yearanal2, yearanal3, yearangi, yearanta, yearanta2, yearanti, yearanti2, yearbblo, yearbblo2, yearcalc,
yearcalc2, yearcalreg, yearcarb, yearchol, yearchol2, yearchol3, yearclau1, yearclau2, yearclop, yearcom1, yearcom2, yearcom3, yearcort, yearcorthorm2, yearderm,
yeardig, yeardig2, yeardig3, yeardig4, yeardipy, yeardiur, yeardiur2, yeardiur3, yearerec, yeareye, yeargluc, yeargluc2, yeargluc3, yeargluc4, yeargrel, yearinsu,
yeariron, yeariron2, yearneur, yearneur2, yearneur3, yearneur4, yearnitr, yearnitr2, yearOR_bin_2010, YearOR_per2years, yearotant, yearotcor, yearoth2, yearothe,
yearpros, yearpsy5, yearren, yearresp, yearrheu, yearrheu2, yearrheu3, yearsta2, yearstat, yearthro, yearthyr, yearthyr2, yearvit2, yearvita, Yrs.no.smoking,
Yrs.smoking
The following objects are masked from AEDB (pos = 4):
ABI_70, ABI_max, ABI_mean, ABI_min, ABI_OP, ablock, ablock2, ablock3, aceinhib, aceinhib2, acetylsa, Adiponectin_pg_ug_2015, AE_AAA_bijzonderheden, Age, Age_Q,
AgeSQR, aid, Aldosteron_recode, alg10201, alg10202, alg10203, alg10204, alg10205, alg105, alg106, alg109, alg110, alg113, alg114, alg115, ALOX5, analg2, analg3,
analgeti, Ang2, angioii, ANGPT2, anti_apoA1_IgG, anti_apoA1_index, anti_apoA1_na, antiall, antiall2, antiarrh, antiarrh2, ANXA2, AP_Dx, AP_Dx1, AP_Dx2, APOB,
artercon, Artery_summary, arteryop, AsymptSympt, AsymptSympt2G, bblock, bblock2, blocko, blocksnr, BMI, BMI_US, BMI_WHO, BMI30ormore, brain401, brain402, brain403,
brain404, brain405, brain406, brain407, brain408, brain409, brain410, brain411, brain412, brain413, brn40701, bspoed, CAD_Dx, CAD_Dx1, CAD_Dx2, CAD_history,
CADPAOD_history, Calc.bin, calcification, calcium, calcium2, calreg, carbasal, cardioembolic, Caspase3_7, CAV1, CD44, CD44V3, CEA_or_CAS, CEL, CFD_recalc, cholverl,
cholverl2, cholverl3, CI_history, clau1, clau2, Claudication, clopidog, CML, collagen, Collagen.bin, combi1, combi2, combi3, comorbidity.DM, concablo, concablo2,
concablo3, concace2, concacei, concacet, concalle, concanal, concanal2, concanal3, concangi, concanta2, concanti, concanti2, concbblo, concbblo2, conccalc,
conccalc2, conccalreg, conccarb, concchol, concchol2, concchol3, concclau1, concclau2, concclop, conccom1, conccom2, conccom3, conccort, conccorthorm2, concderm,
concdig, concdig2, concdig3, concdig4, concdipy, concdiur, concdiur2, concdiur3, concerec, conceye, concgluc, concgluc2, concgluc3, concgluc4, concgrel, concinsu,
conciron, conciron2, concneur, concneur2, concneur3, concneur4, concnitr, concnitr2, concotant, concotcor, concoth2, concothe, concpros, concpsy5, concren,
concresp, concrheu, concrheu2, concrheu3, concsta2, concstat, concthro, concthyr, concthyr2, concvit2, concvita, Contralateral_surgery, conwhen, corticos,
cortihorm2, creat, crp_all, CRP_avg, CRP_dif, crp_source, CRP_var, CST3_pg_ug, CST3_serum_luminex, CTGF, cTNI_plasma, CTSA, CTSB, CTSL1, CTSS, cyr61,
date_ic_patient, date_ic_researcher, Date.of.birth, date.previous.operation, date1yr, date3mon, dateapprox_latest, dateapprox_worst, dateapprox1, dateapprox2,
dateapprox3, dateapprox4, dateend1, dateend2, dateend3, dateend4, dateend5, dateend6, dateexact_latest, dateexact_worst, dateexact1, dateexact2, dateexact3,
dateexact4, dateok, dermacor, diastoli, diet801, diet802, diet803, diet804, diet805, diet806, diet807, diet808, diet809, diet810, diet811, diet812, diet813,
diet814, diet815, diet816, diet817, diet818, diet819, diet820, diet821, diet822, diet823, diet824, dipyridi, diuretic, diuretic2, diuretic3, DM, DM.composite,
duaalantiplatelet, duplend, eaindexl, eaindexr, eCigarettes, edaplaqu_recalc, edavrspl, EGR, EMMPRIN_45kD, EMMPRIN_58kD, ENDOGLIN, endpoint1, endpoint2, endpoint3,
endpoint4, endpoint5, endpoint6, Eotaxin1, EP_CAD, ep_cad_t_30days, ep_cad_t_3years, EP_CAD_time, ep_cad.30days, EP_CI, ep_ci_t_30days, ep_ci_t_3years, EP_CI_time,
ep_com_t_30days, ep_com_t_3years, EP_composite, EP_composite_time, EP_coronary, ep_coronary_t_3years, EP_coronary_time, EP_CVdeath, ep_cvdeath_t_30days,
ep_cvdeath_t_3years, EP_CVdeath_time, EP_death, ep_death_t_30days, ep_death_t_3years, EP_death_time, EP_fatalCVA, ep_fatalCVA_t_30days, ep_fatalCVA_t_3years,
EP_fatalCVA_time, EP_hemorrhagic_stroke, ep_hemorrhagic_stroke_t_3years, EP_hemorrhagic_stroke_time, ep_hemorrhagic_stroke.3years, EP_ischemic_stroke,
ep_ischemic_stroke_t_3years, EP_ischemic_stroke_time, ep_ischemic_stroke.3years, EP_leg_amputation, EP_leg_amputation_time, ep_legamputation_t_30days,
ep_legamputation_t_3years, EP_major, ep_major_t_30days, ep_major_t_3years, EP_major_time, EP_MI, ep_mi_t_30days, ep_mi_t_3years, EP_MI_time, EP_nonstroke_event,
EP_nonstroke_event_time, ep_nonstroke_t_3years, EP_peripheral, ep_peripheral_t_30days, ep_peripheral_t_3years, EP_peripheral_time, EP_pta, ep_pta_t_30days,
ep_pta_t_3years, EP_pta_time, EP_stroke, ep_stroke_t_30days, ep_stroke_t_3years, EP_stroke_time, EP_strokeCVdeath, ep_strokeCVdeath_t_30days,
ep_strokeCVdeath_t_3years, EP_strokeCVdeath_time, EP_strokedeath, ep_strokedeath_t_30days, ep_strokedeath_t_3years, EP_strokedeath_time, ePackYearsSmoking,
epcad.3years, epci.30days, epci.3years, epcom.30days, epcom.3years, epcoronary.3years, epcvdeath.30days, epcvdeath.3years, epdeath.30days, epdeath.3years,
epfatalCVA.30days, epfatalCVA.3years, eplegamputation.30days, eplegamputation.3years, epmajor.30days, epmajor.3years, epmi.30days, epmi.3years, epnonstroke.3years,
epperipheral.30days, epperipheral.3years, eppta.30days, eppta.3years, epstroke.30days, epstroke.3years, epstrokeCVdeath.30days, epstrokeCVdeath.3years,
epstrokedeath.30days, epstrokedeath.3years, erec, Estradiol, everstroke_composite, Everstroke_Ipsilateral, exer901, exer902, exer903, exer904, exer905, exer906,
exer9071, exer9072, exer9073, exer9074, exer9075, exer9076, exer908, exer909, exer910, eyedrop, EZis, FABP_serum, FABP4, FABP4_pg_ug, FABP4_serum_luminex, fat,
Fat.bin_10, Fat.bin_40, Femoral.interv, FH_AAA_broth, FH_AAA_comp, FH_AAA_mat, FH_AAA_parent, FH_AAA_pat, FH_AAA_sibling, FH_AAA_sis, FH_amp_broth, FH_amp_comp,
FH_amp_mat, FH_amp_parent, FH_amp_pat, FH_amp_sibling, FH_amp_sis, FH_CAD_broth, FH_CAD_comp, FH_CAD_mat, FH_CAD_parent, FH_CAD_pat, FH_CAD_sibling, FH_CAD_sis,
FH_corcalc_broth, FH_corcalc_comp, FH_corcalc_mat, FH_corcalc_parent, FH_corcalc_pat, FH_corcalc_sibling, FH_corcalc_sis, FH_CVD_broth, FH_CVD_comp, FH_CVD_mat,
FH_CVD_parent, FH_CVD_pat, FH_CVD_sibling, FH_CVD_sis, FH_CVdeath_broth, FH_CVdeath_comp, FH_CVdeath_mat, FH_CVdeath_parent, FH_CVdeath_pat, FH_CVdeath_sibling,
FH_CVdeath_sis, FH_DM_broth, FH_DM_comp, FH_DM_mat, FH_DM_parent, FH_DM_pat, FH_DM_sibling, FH_DM_sis, FH_HC_broth, FH_HC_comp, FH_HC_mat, FH_HC_parent, FH_HC_pat,
FH_HC_sibling, FH_HC_sis, FH_HT_broth, FH_HT_comp, FH_HT_mat, FH_HT_parent, FH_HT_pat, FH_HT_sibling, FH_HT_sis, FH_MI_broth, FH_MI_comp, FH_MI_mat, FH_MI_parent,
FH_MI_pat, FH_MI_sibling, FH_MI_sis, FH_otherCVD_broth, FH_otherCVD_comp, FH_otherCVD_mat, FH_otherCVD_parent, FH_otherCVD_pat, FH_otherCVD_sibling,
FH_otherCVD_sis, FH_PAD_broth, FH_PAD_comp, FH_PAD_mat, FH_PAD_parent, FH_PAD_pat, FH_PAD_sibling, FH_PAD_sis, FH_PAV_broth, FH_PAV_comp, FH_PAV_mat, FH_PAV_parent,
FH_PAV_pat, FH_PAV_sibling, FH_PAV_sis, FH_POB_broth, FH_POB_comp, FH_POB_mat, FH_POB_parent, FH_POB_pat, FH_POB_sibling, FH_POB_sis, FH_risk_broth, FH_risk_comp,
FH_risk_mat, FH_risk_parent, FH_risk_pat, FH_risk_sibling, FH_risk_sis, FH_Stroke_broth, FH_Stroke_comp, FH_Stroke_mat, FH_Stroke_parent, FH_Stroke_pat,
FH_Stroke_sibling, FH_Stroke_sis, FH_tromb_broth, FH_tromb_comp, FH_tromb_mat, FH_tromb_parent, FH_tromb_pat, FH_tromb_sibling, FH_tromb_sis, filter_$, folicaci,
followup1, followup2, followup3, Fontaine, FU_check, FU_check_date, FU.cutt.off.30days, FU.cutt.off.3years, FU1JAAR, FU2JAAR, FU3JAAR, FURIN_low, FURIN_up,
GDF15_plasma, geen_med, Gender, GFR_CG, GFR_MDRD, glucose, GR_Segment, GrB_plaque, GrB_serum, grel, GrK_plaque, GrK_serum, GrM_plaque, GrM_serum, HA, hb, HDAC9,
HDL, HDL_2016, HDL_all, HDL_avg, HDL_clinic, HDL_dif, HDL_final, HDL_finalCU, hdl_source, HDL_var, heart300, heart301, heart302, heart303, heart304, heart305,
heart306, heart307, heart308, heart309, heart310, heart311, heart312, heart313, heart314, heart315, heart316, heart317, heart318, heart319, heart320, heart321,
heart322, heart323, heart324, heart325, heart326, heart327, heart328, HIF1A, ho1, homocys, Hospital, hrt31301, hsCRP_plasma, ht, HYAL55KD, HYALURON,
Hypertension.composite, Hypertension.drugs, Hypertension1, Hypertension2, IL1_Beta, IL10, IL12, IL13, IL17, IL2, IL21, IL4, IL5, IL6, IL6_pg_ug_2015,
IL6R_pg_ug_2015, IL8, IL8_pg_ug_2015, IL9, indexsymptoms_latest, indexsymptoms_latest_4g, indexsymptoms_worst, indexsymptoms_worst_4g, INFG, informedconsent,
insulin, insuline, INVULDAT, IP10, IPH_extended.bin, IPH.bin, ironfoli, ironfoli2, KDOQI, latest, LDL, LDL_2016, LDL_all, LDL_avg, LDL_clinic, LDL_dif, LDL_final,
LDL_finalCU, ldl_source, LDL_var, leg501, leg502, leg503, leg504, leg505, leg506, leg507, leg508, leg509, leg510, leg511, leg512, leg513, leg514, leg515, leg516,
leg517, leg518, leg519, leg520, LMW1STME, LTB4, LTB4R, macmean0, macrophages, macrophages_location, Macrophages.bin, MAP, Mast_cells_plaque, max.followup, MCP1,
MCP1_pg_ug_2015, MCSF_pg_ug_2015, MDC, Med_notes, Med.ablock, Med.ACE_inh, Med.acetylsal, Med.acetylsal_Combi1, Med.acetylsal_Combi2, Med.acetylsal_Combi3,
Med.ADPinh, Med.all.antiplatelet, Med.angiot2.antag, Med.antiarrh, Med.anticoagulants, Med.ascal, Med.aspirin.derived, Med.bblocker, Med.calc_antag,
Med.dipyridamole, Med.diuretic, Med.LLD, Med.nitrate, Med.otheranthyp, Med.renin, Med.statin, Med.statin.derived, Med.Statin.LLD, Med.statin2, media, MG_H1, MI_Dx,
MI_Dx1, MI_Dx2, MIF, MIG, MIP1a, miRNA100_RNU19, miRNA100_RNU48, miRNA155_RNU19, miRNA155_RNU48, MMP14, MMP2, MMP2TIMP2, MMP8, MMP9, MMP9TIMP1, MPO_plasma, MRP_14,
MRP_8, MRP_8_14C, MRP_8_14C_buhlmann, MRP14_plasma, MRP8_14C_plasma, MRP8_plasma, negatibl, neuropsy, neuropsy2, neuropsy3, neuropsy4, neurpsy5, neutrophils, NGAL,
NGAL_low, NGAL_MMP9_complex, NGAL_MMP9_local, NGAL_MMP9_peripheral, NGAL_total, NGAL_up, nitrate, nitrate2, NOD1, NOD2, nogobt1_recalculated, NTproBNP_plasma,
Number_Events_Sorter, Number_Sorted_CD14, Number_Sorted_CD20, Number_Sorted_CD4_Cells, Number_Sorted_CD8_Cells, oac701, oac702, oac70305, oac704, oac705, oac706,
oac707, oac708, oac709, oac710, oac711, oac712, oac713, oac714, OKyear, OPG, OPG_plasma, OPN, OPN_2013, OPN_plasma, OR_blood, Oral.glucose.inh, oralgluc, oralgluc2,
oralgluc3, oralgluc4, ORyear, othanthyp, othcoron, other, other2, OverallPlaquePhenotype, PAI1_pg_ug_2015, PAOD, PARC, patch, PCSK9_plasma, PDGF_BB_plasma,
Percentage_CD14, Percentage_CD20, Percentage_CD4, Percentage_CD8, Peripheral.interv, PKC, PLA2_plasma, plaquephenotype, positibl, PrimaryLast, PrimaryLast1,
prostagl, PulsePressure, qual01, qual02, qual0301, qual0302, qual0303, qual0304, qual0305, qual0306, qual0307, qual0308, qual0309, qual0310, qual0401, qual0402,
qual0403, qual0404, qual0501, qual0502, qual0503, qual06, qual07, qual08, qual0901, qual0902, qual0903, qual0904, qual0905, qual0906, qual0907, qual0908, qual0909,
qual1010, qual1101, qual1102, qual1103, qual1104, RAAS_med, RANTES, RANTES_pg_ug_2015, RANTES_plasma, Ras, RE50_01, RE70_01, Renine_recode, renineinh, restenos,
restenosisOK, rheuma, rheuma2, rheuma3, risk601, risk602, risk603, risk604, risk605, risk606, risk607, risk608, risk609, risk610, risk611, risk612, risk613,
risk614, risk615, risk616, risk617, risk618, risk619, risk620, SHBG, sICAM1, SMAD1_5_8, SMAD2, SMAD3, smc, smc_location, smc_macrophages_ratio, SMC.bin, smcmean0,
SmokerCurrent, SmokingReported, SmokingYearOR, stat3P, statin2, statines, ste3mext, sten1yr, sten3mo, stenose, stenosis_con_bin, Stenosis_contralateral,
Stenosis_ipsilateral, Stroke_Dx, Stroke_eitherside, Stroke_history, Stroke_Symptoms, StrokeTIA_Dx, StrokeTIA_history, StrokeTIA_Symptoms, STUDY_NUMBER, sympt,
Sympt_latest, Sympt_worst, sympt1, sympt2, sympt3, sympt4, Symptoms.3g, Symptoms.4g, Symptoms.5G, systolic, T_NUMBER, TARC, TAT_plasma, TC_2016, TC_all, TC_avg,
TC_clinic, TC_dif, TC_final, TC_finalCU, TC_var, Testosterone, TG_2016, TG_all, TG_avg, TG_clinic, TG_dif, TG_final, TG_finalCU, TG_var, TGF, TGFB, thrombos,
thrombus, thrombus_location, thrombus_new, thrombus_organization, thrombus_organization_v2, thrombus_percentage, thyros2, thyrosta, Time_event_OR, TimeOR_latest,
TimeOR_latest_4g, TimeOR_worst, TimeOR_worst_4g, TIMP1, TIMP2, TISNOW, TNFA, totalchol, totalcholesterol_source, tractdig, tractdig2, tractdig3, tractdig4,
tractres, Treatment.DM, TREM1, triglyceride_source, triglyceriden, Trop1, Trop1DT, Trop2, Trop2DT, Trop3, Trop3DT, TropmaxpostOK, TropoMax, TropoMaxDT,
tropomaxpositief, TSratio_blood, TSratio_plaque, UPID, validation_date, validation1, validation2, validation3, validation4, validation5, validation6, VAR00001,
VEFGA, VEGFA_plasma, vegfa422, vessel_density, vessel_density_additional, vessel_density_averaged, vessel_density_Timo2012, vessel_density_Timo2012_2,
vessel_density_Timo2013, vitamin, vitamin2, vitb12, VRAGENLIJST, vWF_plasma, WBC_THAW, Which.femoral.artery, Whichoperation, writtenIC, yearablo, yearablo2,
yearablo3, yearace, yearace2, yearacet, yearanal, yearanal2, yearanal3, yearangi, yearanta, yearanta2, yearanti, yearanti2, yearbblo, yearbblo2, yearcalc,
yearcalc2, yearcalreg, yearcarb, yearchol, yearchol2, yearchol3, yearclau1, yearclau2, yearclop, yearcom1, yearcom2, yearcom3, yearcort, yearcorthorm2, yearderm,
yeardig, yeardig2, yeardig3, yeardig4, yeardipy, yeardiur, yeardiur2, yeardiur3, yearerec, yeareye, yeargluc, yeargluc2, yeargluc3, yeargluc4, yeargrel, yearinsu,
yeariron, yeariron2, yearneur, yearneur2, yearneur3, yearneur4, yearnitr, yearnitr2, yearOR_bin_2010, YearOR_per2years, yearotant, yearotcor, yearoth2, yearothe,
yearpros, yearpsy5, yearren, yearresp, yearrheu, yearrheu2, yearrheu3, yearsta2, yearstat, yearthro, yearthyr, yearthyr2, yearvit2, yearvita, Yrs.no.smoking,
Yrs.smoking
The following objects are masked from AEDB (pos = 5):
ABI_70, ABI_max, ABI_mean, ABI_min, ABI_OP, ablock, ablock2, ablock3, aceinhib, aceinhib2, acetylsa, Adiponectin_pg_ug_2015, AE_AAA_bijzonderheden, Age, Age_Q,
AgeSQR, aid, Aldosteron_recode, alg10201, alg10202, alg10203, alg10204, alg10205, alg105, alg106, alg109, alg110, alg113, alg114, alg115, ALOX5, analg2, analg3,
analgeti, Ang2, angioii, ANGPT2, anti_apoA1_IgG, anti_apoA1_index, anti_apoA1_na, antiall, antiall2, antiarrh, antiarrh2, ANXA2, AP_Dx, AP_Dx1, AP_Dx2, APOB,
artercon, Artery_summary, arteryop, AsymptSympt, AsymptSympt2G, bblock, bblock2, blocko, blocksnr, BMI, BMI_US, BMI_WHO, BMI30ormore, brain401, brain402, brain403,
brain404, brain405, brain406, brain407, brain408, brain409, brain410, brain411, brain412, brain413, brn40701, bspoed, CAD_Dx, CAD_Dx1, CAD_Dx2, CAD_history,
CADPAOD_history, Calc.bin, calcification, calcium, calcium2, calreg, carbasal, cardioembolic, Caspase3_7, CAV1, CD44, CD44V3, CEA_or_CAS, CEL, CFD_recalc, cholverl,
cholverl2, cholverl3, CI_history, clau1, clau2, Claudication, clopidog, CML, collagen, Collagen.bin, combi1, combi2, combi3, comorbidity.DM, concablo, concablo2,
concablo3, concace2, concacei, concacet, concalle, concanal, concanal2, concanal3, concangi, concanta2, concanti, concanti2, concbblo, concbblo2, conccalc,
conccalc2, conccalreg, conccarb, concchol, concchol2, concchol3, concclau1, concclau2, concclop, conccom1, conccom2, conccom3, conccort, conccorthorm2, concderm,
concdig, concdig2, concdig3, concdig4, concdipy, concdiur, concdiur2, concdiur3, concerec, conceye, concgluc, concgluc2, concgluc3, concgluc4, concgrel, concinsu,
conciron, conciron2, concneur, concneur2, concneur3, concneur4, concnitr, concnitr2, concotant, concotcor, concoth2, concothe, concpros, concpsy5, concren,
concresp, concrheu, concrheu2, concrheu3, concsta2, concstat, concthro, concthyr, concthyr2, concvit2, concvita, Contralateral_surgery, conwhen, corticos,
cortihorm2, creat, crp_all, CRP_avg, CRP_dif, crp_source, CRP_var, CST3_pg_ug, CST3_serum_luminex, CTGF, cTNI_plasma, CTSA, CTSB, CTSL1, CTSS, cyr61,
date_ic_patient, date_ic_researcher, Date.of.birth, date.previous.operation, date1yr, date3mon, dateapprox_latest, dateapprox_worst, dateapprox1, dateapprox2,
dateapprox3, dateapprox4, dateend1, dateend2, dateend3, dateend4, dateend5, dateend6, dateexact_latest, dateexact_worst, dateexact1, dateexact2, dateexact3,
dateexact4, dateok, dermacor, diastoli, diet801, diet802, diet803, diet804, diet805, diet806, diet807, diet808, diet809, diet810, diet811, diet812, diet813,
diet814, diet815, diet816, diet817, diet818, diet819, diet820, diet821, diet822, diet823, diet824, dipyridi, diuretic, diuretic2, diuretic3, DM, DM.composite,
duaalantiplatelet, duplend, eaindexl, eaindexr, eCigarettes, edaplaqu_recalc, edavrspl, EGR, EMMPRIN_45kD, EMMPRIN_58kD, ENDOGLIN, endpoint1, endpoint2, endpoint3,
endpoint4, endpoint5, endpoint6, Eotaxin1, EP_CAD, ep_cad_t_30days, ep_cad_t_3years, EP_CAD_time, ep_cad.30days, EP_CI, ep_ci_t_30days, ep_ci_t_3years, EP_CI_time,
ep_com_t_30days, ep_com_t_3years, EP_composite, EP_composite_time, EP_coronary, ep_coronary_t_3years, EP_coronary_time, EP_CVdeath, ep_cvdeath_t_30days,
ep_cvdeath_t_3years, EP_CVdeath_time, EP_death, ep_death_t_30days, ep_death_t_3years, EP_death_time, EP_fatalCVA, ep_fatalCVA_t_30days, ep_fatalCVA_t_3years,
EP_fatalCVA_time, EP_hemorrhagic_stroke, ep_hemorrhagic_stroke_t_3years, EP_hemorrhagic_stroke_time, ep_hemorrhagic_stroke.3years, EP_ischemic_stroke,
ep_ischemic_stroke_t_3years, EP_ischemic_stroke_time, ep_ischemic_stroke.3years, EP_leg_amputation, EP_leg_amputation_time, ep_legamputation_t_30days,
ep_legamputation_t_3years, EP_major, ep_major_t_30days, ep_major_t_3years, EP_major_time, EP_MI, ep_mi_t_30days, ep_mi_t_3years, EP_MI_time, EP_nonstroke_event,
EP_nonstroke_event_time, ep_nonstroke_t_3years, EP_peripheral, ep_peripheral_t_30days, ep_peripheral_t_3years, EP_peripheral_time, EP_pta, ep_pta_t_30days,
ep_pta_t_3years, EP_pta_time, EP_stroke, ep_stroke_t_30days, ep_stroke_t_3years, EP_stroke_time, EP_strokeCVdeath, ep_strokeCVdeath_t_30days,
ep_strokeCVdeath_t_3years, EP_strokeCVdeath_time, EP_strokedeath, ep_strokedeath_t_30days, ep_strokedeath_t_3years, EP_strokedeath_time, ePackYearsSmoking,
epcad.3years, epci.30days, epci.3years, epcom.30days, epcom.3years, epcoronary.3years, epcvdeath.30days, epcvdeath.3years, epdeath.30days, epdeath.3years,
epfatalCVA.30days, epfatalCVA.3years, eplegamputation.30days, eplegamputation.3years, epmajor.30days, epmajor.3years, epmi.30days, epmi.3years, epnonstroke.3years,
epperipheral.30days, epperipheral.3years, eppta.30days, eppta.3years, epstroke.30days, epstroke.3years, epstrokeCVdeath.30days, epstrokeCVdeath.3years,
epstrokedeath.30days, epstrokedeath.3years, erec, Estradiol, everstroke_composite, Everstroke_Ipsilateral, exer901, exer902, exer903, exer904, exer905, exer906,
exer9071, exer9072, exer9073, exer9074, exer9075, exer9076, exer908, exer909, exer910, eyedrop, EZis, FABP_serum, FABP4, FABP4_pg_ug, FABP4_serum_luminex, fat,
Fat.bin_10, Fat.bin_40, Femoral.interv, FH_AAA_broth, FH_AAA_comp, FH_AAA_mat, FH_AAA_parent, FH_AAA_pat, FH_AAA_sibling, FH_AAA_sis, FH_amp_broth, FH_amp_comp,
FH_amp_mat, FH_amp_parent, FH_amp_pat, FH_amp_sibling, FH_amp_sis, FH_CAD_broth, FH_CAD_comp, FH_CAD_mat, FH_CAD_parent, FH_CAD_pat, FH_CAD_sibling, FH_CAD_sis,
FH_corcalc_broth, FH_corcalc_comp, FH_corcalc_mat, FH_corcalc_parent, FH_corcalc_pat, FH_corcalc_sibling, FH_corcalc_sis, FH_CVD_broth, FH_CVD_comp, FH_CVD_mat,
FH_CVD_parent, FH_CVD_pat, FH_CVD_sibling, FH_CVD_sis, FH_CVdeath_broth, FH_CVdeath_comp, FH_CVdeath_mat, FH_CVdeath_parent, FH_CVdeath_pat, FH_CVdeath_sibling,
FH_CVdeath_sis, FH_DM_broth, FH_DM_comp, FH_DM_mat, FH_DM_parent, FH_DM_pat, FH_DM_sibling, FH_DM_sis, FH_HC_broth, FH_HC_comp, FH_HC_mat, FH_HC_parent, FH_HC_pat,
FH_HC_sibling, FH_HC_sis, FH_HT_broth, FH_HT_comp, FH_HT_mat, FH_HT_parent, FH_HT_pat, FH_HT_sibling, FH_HT_sis, FH_MI_broth, FH_MI_comp, FH_MI_mat, FH_MI_parent,
FH_MI_pat, FH_MI_sibling, FH_MI_sis, FH_otherCVD_broth, FH_otherCVD_comp, FH_otherCVD_mat, FH_otherCVD_parent, FH_otherCVD_pat, FH_otherCVD_sibling,
FH_otherCVD_sis, FH_PAD_broth, FH_PAD_comp, FH_PAD_mat, FH_PAD_parent, FH_PAD_pat, FH_PAD_sibling, FH_PAD_sis, FH_PAV_broth, FH_PAV_comp, FH_PAV_mat, FH_PAV_parent,
FH_PAV_pat, FH_PAV_sibling, FH_PAV_sis, FH_POB_broth, FH_POB_comp, FH_POB_mat, FH_POB_parent, FH_POB_pat, FH_POB_sibling, FH_POB_sis, FH_risk_broth, FH_risk_comp,
FH_risk_mat, FH_risk_parent, FH_risk_pat, FH_risk_sibling, FH_risk_sis, FH_Stroke_broth, FH_Stroke_comp, FH_Stroke_mat, FH_Stroke_parent, FH_Stroke_pat,
FH_Stroke_sibling, FH_Stroke_sis, FH_tromb_broth, FH_tromb_comp, FH_tromb_mat, FH_tromb_parent, FH_tromb_pat, FH_tromb_sibling, FH_tromb_sis, filter_$, folicaci,
followup1, followup2, followup3, Fontaine, FU_check, FU_check_date, FU.cutt.off.30days, FU.cutt.off.3years, FU1JAAR, FU2JAAR, FU3JAAR, FURIN_low, FURIN_up,
GDF15_plasma, geen_med, Gender, GFR_CG, GFR_MDRD, glucose, GR_Segment, GrB_plaque, GrB_serum, grel, GrK_plaque, GrK_serum, GrM_plaque, GrM_serum, HA, hb, HDAC9,
HDL, HDL_2016, HDL_all, HDL_avg, HDL_clinic, HDL_dif, HDL_final, HDL_finalCU, hdl_source, HDL_var, heart300, heart301, heart302, heart303, heart304, heart305,
heart306, heart307, heart308, heart309, heart310, heart311, heart312, heart313, heart314, heart315, heart316, heart317, heart318, heart319, heart320, heart321,
heart322, heart323, heart324, heart325, heart326, heart327, heart328, HIF1A, ho1, homocys, Hospital, hrt31301, hsCRP_plasma, ht, HYAL55KD, HYALURON,
Hypertension.composite, Hypertension.drugs, Hypertension1, Hypertension2, IL1_Beta, IL10, IL12, IL13, IL17, IL2, IL21, IL4, IL5, IL6, IL6_pg_ug_2015,
IL6R_pg_ug_2015, IL8, IL8_pg_ug_2015, IL9, indexsymptoms_latest, indexsymptoms_latest_4g, indexsymptoms_worst, indexsymptoms_worst_4g, INFG, informedconsent,
insulin, insuline, INVULDAT, IP10, IPH_extended.bin, IPH.bin, ironfoli, ironfoli2, KDOQI, latest, LDL, LDL_2016, LDL_all, LDL_avg, LDL_clinic, LDL_dif, LDL_final,
LDL_finalCU, ldl_source, LDL_var, leg501, leg502, leg503, leg504, leg505, leg506, leg507, leg508, leg509, leg510, leg511, leg512, leg513, leg514, leg515, leg516,
leg517, leg518, leg519, leg520, LMW1STME, LTB4, LTB4R, macmean0, macrophages, macrophages_location, Macrophages.bin, MAP, Mast_cells_plaque, max.followup, MCP1,
MCP1_pg_ug_2015, MCSF_pg_ug_2015, MDC, Med_notes, Med.ablock, Med.ACE_inh, Med.acetylsal, Med.acetylsal_Combi1, Med.acetylsal_Combi2, Med.acetylsal_Combi3,
Med.ADPinh, Med.all.antiplatelet, Med.angiot2.antag, Med.antiarrh, Med.anticoagulants, Med.ascal, Med.aspirin.derived, Med.bblocker, Med.calc_antag,
Med.dipyridamole, Med.diuretic, Med.LLD, Med.nitrate, Med.otheranthyp, Med.renin, Med.statin, Med.statin.derived, Med.Statin.LLD, Med.statin2, media, MG_H1, MI_Dx,
MI_Dx1, MI_Dx2, MIF, MIG, MIP1a, miRNA100_RNU19, miRNA100_RNU48, miRNA155_RNU19, miRNA155_RNU48, MMP14, MMP2, MMP2TIMP2, MMP8, MMP9, MMP9TIMP1, MPO_plasma, MRP_14,
MRP_8, MRP_8_14C, MRP_8_14C_buhlmann, MRP14_plasma, MRP8_14C_plasma, MRP8_plasma, negatibl, neuropsy, neuropsy2, neuropsy3, neuropsy4, neurpsy5, neutrophils, NGAL,
NGAL_low, NGAL_MMP9_complex, NGAL_MMP9_local, NGAL_MMP9_peripheral, NGAL_total, NGAL_up, nitrate, nitrate2, NOD1, NOD2, nogobt1_recalculated, NTproBNP_plasma,
Number_Events_Sorter, Number_Sorted_CD14, Number_Sorted_CD20, Number_Sorted_CD4_Cells, Number_Sorted_CD8_Cells, oac701, oac702, oac70305, oac704, oac705, oac706,
oac707, oac708, oac709, oac710, oac711, oac712, oac713, oac714, OKyear, OPG, OPG_plasma, OPN, OPN_2013, OPN_plasma, OR_blood, Oral.glucose.inh, oralgluc, oralgluc2,
oralgluc3, oralgluc4, ORyear, othanthyp, othcoron, other, other2, OverallPlaquePhenotype, PAI1_pg_ug_2015, PAOD, PARC, patch, PCSK9_plasma, PDGF_BB_plasma,
Percentage_CD14, Percentage_CD20, Percentage_CD4, Percentage_CD8, Peripheral.interv, PKC, PLA2_plasma, plaquephenotype, positibl, PrimaryLast, PrimaryLast1,
prostagl, PulsePressure, qual01, qual02, qual0301, qual0302, qual0303, qual0304, qual0305, qual0306, qual0307, qual0308, qual0309, qual0310, qual0401, qual0402,
qual0403, qual0404, qual0501, qual0502, qual0503, qual06, qual07, qual08, qual0901, qual0902, qual0903, qual0904, qual0905, qual0906, qual0907, qual0908, qual0909,
qual1010, qual1101, qual1102, qual1103, qual1104, RAAS_med, RANTES, RANTES_pg_ug_2015, RANTES_plasma, Ras, RE50_01, RE70_01, Renine_recode, renineinh, restenos,
restenosisOK, rheuma, rheuma2, rheuma3, risk601, risk602, risk603, risk604, risk605, risk606, risk607, risk608, risk609, risk610, risk611, risk612, risk613,
risk614, risk615, risk616, risk617, risk618, risk619, risk620, SHBG, sICAM1, SMAD1_5_8, SMAD2, SMAD3, smc, smc_location, smc_macrophages_ratio, SMC.bin, smcmean0,
SmokerCurrent, SmokingReported, SmokingYearOR, stat3P, statin2, statines, ste3mext, sten1yr, sten3mo, stenose, stenosis_con_bin, Stenosis_contralateral,
Stenosis_ipsilateral, Stroke_Dx, Stroke_eitherside, Stroke_history, Stroke_Symptoms, StrokeTIA_Dx, StrokeTIA_history, StrokeTIA_Symptoms, STUDY_NUMBER, sympt,
Sympt_latest, Sympt_worst, sympt1, sympt2, sympt3, sympt4, Symptoms.3g, Symptoms.4g, Symptoms.5G, systolic, T_NUMBER, TARC, TAT_plasma, TC_2016, TC_all, TC_avg,
TC_clinic, TC_dif, TC_final, TC_finalCU, TC_var, Testosterone, TG_2016, TG_all, TG_avg, TG_clinic, TG_dif, TG_final, TG_finalCU, TG_var, TGF, TGFB, thrombos,
thrombus, thrombus_location, thrombus_new, thrombus_organization, thrombus_organization_v2, thrombus_percentage, thyros2, thyrosta, Time_event_OR, TimeOR_latest,
TimeOR_latest_4g, TimeOR_worst, TimeOR_worst_4g, TIMP1, TIMP2, TISNOW, TNFA, totalchol, totalcholesterol_source, tractdig, tractdig2, tractdig3, tractdig4,
tractres, Treatment.DM, TREM1, triglyceride_source, triglyceriden, Trop1, Trop1DT, Trop2, Trop2DT, Trop3, Trop3DT, TropmaxpostOK, TropoMax, TropoMaxDT,
tropomaxpositief, TSratio_blood, TSratio_plaque, UPID, validation_date, validation1, validation2, validation3, validation4, validation5, validation6, VAR00001,
VEFGA, VEGFA_plasma, vegfa422, vessel_density, vessel_density_additional, vessel_density_averaged, vessel_density_Timo2012, vessel_density_Timo2012_2,
vessel_density_Timo2013, vitamin, vitamin2, vitb12, VRAGENLIJST, vWF_plasma, WBC_THAW, Which.femoral.artery, Whichoperation, writtenIC, yearablo, yearablo2,
yearablo3, yearace, yearace2, yearacet, yearanal, yearanal2, yearanal3, yearangi, yearanta, yearanta2, yearanti, yearanti2, yearbblo, yearbblo2, yearcalc,
yearcalc2, yearcalreg, yearcarb, yearchol, yearchol2, yearchol3, yearclau1, yearclau2, yearclop, yearcom1, yearcom2, yearcom3, yearcort, yearcorthorm2, yearderm,
yeardig, yeardig2, yeardig3, yeardig4, yeardipy, yeardiur, yeardiur2, yeardiur3, yearerec, yeareye, yeargluc, yeargluc2, yeargluc3, yeargluc4, yeargrel, yearinsu,
yeariron, yeariron2, yearneur, yearneur2, yearneur3, yearneur4, yearnitr, yearnitr2, yearOR_bin_2010, YearOR_per2years, yearotant, yearotcor, yearoth2, yearothe,
yearpros, yearpsy5, yearren, yearresp, yearrheu, yearrheu2, yearrheu3, yearsta2, yearstat, yearthro, yearthyr, yearthyr2, yearvit2, yearvita, Yrs.no.smoking,
Yrs.smoking
The following objects are masked from AEDB (pos = 6):
ABI_70, ABI_max, ABI_mean, ABI_min, ABI_OP, ablock, ablock2, ablock3, aceinhib, aceinhib2, acetylsa, Adiponectin_pg_ug_2015, AE_AAA_bijzonderheden, Age, Age_Q,
AgeSQR, aid, Aldosteron_recode, alg10201, alg10202, alg10203, alg10204, alg10205, alg105, alg106, alg109, alg110, alg113, alg114, alg115, ALOX5, analg2, analg3,
analgeti, Ang2, angioii, ANGPT2, anti_apoA1_IgG, anti_apoA1_index, anti_apoA1_na, antiall, antiall2, antiarrh, antiarrh2, ANXA2, AP_Dx, AP_Dx1, AP_Dx2, APOB,
artercon, Artery_summary, arteryop, AsymptSympt, AsymptSympt2G, bblock, bblock2, blocko, blocksnr, BMI, BMI_US, BMI_WHO, BMI30ormore, brain401, brain402, brain403,
brain404, brain405, brain406, brain407, brain408, brain409, brain410, brain411, brain412, brain413, brn40701, bspoed, CAD_Dx, CAD_Dx1, CAD_Dx2, CAD_history,
CADPAOD_history, Calc.bin, calcification, calcium, calcium2, calreg, carbasal, cardioembolic, Caspase3_7, CAV1, CD44, CD44V3, CEA_or_CAS, CEL, CFD_recalc, cholverl,
cholverl2, cholverl3, CI_history, clau1, clau2, Claudication, clopidog, CML, collagen, Collagen.bin, combi1, combi2, combi3, comorbidity.DM, concablo, concablo2,
concablo3, concace2, concacei, concacet, concalle, concanal, concanal2, concanal3, concangi, concanta2, concanti, concanti2, concbblo, concbblo2, conccalc,
conccalc2, conccalreg, conccarb, concchol, concchol2, concchol3, concclau1, concclau2, concclop, conccom1, conccom2, conccom3, conccort, conccorthorm2, concderm,
concdig, concdig2, concdig3, concdig4, concdipy, concdiur, concdiur2, concdiur3, concerec, conceye, concgluc, concgluc2, concgluc3, concgluc4, concgrel, concinsu,
conciron, conciron2, concneur, concneur2, concneur3, concneur4, concnitr, concnitr2, concotant, concotcor, concoth2, concothe, concpros, concpsy5, concren,
concresp, concrheu, concrheu2, concrheu3, concsta2, concstat, concthro, concthyr, concthyr2, concvit2, concvita, Contralateral_surgery, conwhen, corticos,
cortihorm2, creat, crp_all, CRP_avg, CRP_dif, crp_source, CRP_var, CST3_pg_ug, CST3_serum_luminex, CTGF, cTNI_plasma, CTSA, CTSB, CTSL1, CTSS, cyr61,
date_ic_patient, date_ic_researcher, Date.of.birth, date.previous.operation, date1yr, date3mon, dateapprox_latest, dateapprox_worst, dateapprox1, dateapprox2,
dateapprox3, dateapprox4, dateend1, dateend2, dateend3, dateend4, dateend5, dateend6, dateexact_latest, dateexact_worst, dateexact1, dateexact2, dateexact3,
dateexact4, dateok, dermacor, diastoli, diet801, diet802, diet803, diet804, diet805, diet806, diet807, diet808, diet809, diet810, diet811, diet812, diet813,
diet814, diet815, diet816, diet817, diet818, diet819, diet820, diet821, diet822, diet823, diet824, dipyridi, diuretic, diuretic2, diuretic3, DM, DM.composite,
duaalantiplatelet, duplend, eaindexl, eaindexr, eCigarettes, edaplaqu_recalc, edavrspl, EGR, EMMPRIN_45kD, EMMPRIN_58kD, ENDOGLIN, endpoint1, endpoint2, endpoint3,
endpoint4, endpoint5, endpoint6, Eotaxin1, EP_CAD, ep_cad_t_30days, ep_cad_t_3years, EP_CAD_time, ep_cad.30days, EP_CI, ep_ci_t_30days, ep_ci_t_3years, EP_CI_time,
ep_com_t_30days, ep_com_t_3years, EP_composite, EP_composite_time, EP_coronary, ep_coronary_t_3years, EP_coronary_time, EP_CVdeath, ep_cvdeath_t_30days,
ep_cvdeath_t_3years, EP_CVdeath_time, EP_death, ep_death_t_30days, ep_death_t_3years, EP_death_time, EP_fatalCVA, ep_fatalCVA_t_30days, ep_fatalCVA_t_3years,
EP_fatalCVA_time, EP_hemorrhagic_stroke, ep_hemorrhagic_stroke_t_3years, EP_hemorrhagic_stroke_time, ep_hemorrhagic_stroke.3years, EP_ischemic_stroke,
ep_ischemic_stroke_t_3years, EP_ischemic_stroke_time, ep_ischemic_stroke.3years, EP_leg_amputation, EP_leg_amputation_time, ep_legamputation_t_30days,
ep_legamputation_t_3years, EP_major, ep_major_t_30days, ep_major_t_3years, EP_major_time, EP_MI, ep_mi_t_30days, ep_mi_t_3years, EP_MI_time, EP_nonstroke_event,
EP_nonstroke_event_time, ep_nonstroke_t_3years, EP_peripheral, ep_peripheral_t_30days, ep_peripheral_t_3years, EP_peripheral_time, EP_pta, ep_pta_t_30days,
ep_pta_t_3years, EP_pta_time, EP_stroke, ep_stroke_t_30days, ep_stroke_t_3years, EP_stroke_time, EP_strokeCVdeath, ep_strokeCVdeath_t_30days,
ep_strokeCVdeath_t_3years, EP_strokeCVdeath_time, EP_strokedeath, ep_strokedeath_t_30days, ep_strokedeath_t_3years, EP_strokedeath_time, ePackYearsSmoking,
epcad.3years, epci.30days, epci.3years, epcom.30days, epcom.3years, epcoronary.3years, epcvdeath.30days, epcvdeath.3years, epdeath.30days, epdeath.3years,
epfatalCVA.30days, epfatalCVA.3years, eplegamputation.30days, eplegamputation.3years, epmajor.30days, epmajor.3years, epmi.30days, epmi.3years, epnonstroke.3years,
epperipheral.30days, epperipheral.3years, eppta.30days, eppta.3years, epstroke.30days, epstroke.3years, epstrokeCVdeath.30days, epstrokeCVdeath.3years,
epstrokedeath.30days, epstrokedeath.3years, erec, Estradiol, everstroke_composite, Everstroke_Ipsilateral, exer901, exer902, exer903, exer904, exer905, exer906,
exer9071, exer9072, exer9073, exer9074, exer9075, exer9076, exer908, exer909, exer910, eyedrop, EZis, FABP_serum, FABP4, FABP4_pg_ug, FABP4_serum_luminex, fat,
Fat.bin_10, Fat.bin_40, Femoral.interv, FH_AAA_broth, FH_AAA_comp, FH_AAA_mat, FH_AAA_parent, FH_AAA_pat, FH_AAA_sibling, FH_AAA_sis, FH_amp_broth, FH_amp_comp,
FH_amp_mat, FH_amp_parent, FH_amp_pat, FH_amp_sibling, FH_amp_sis, FH_CAD_broth, FH_CAD_comp, FH_CAD_mat, FH_CAD_parent, FH_CAD_pat, FH_CAD_sibling, FH_CAD_sis,
FH_corcalc_broth, FH_corcalc_comp, FH_corcalc_mat, FH_corcalc_parent, FH_corcalc_pat, FH_corcalc_sibling, FH_corcalc_sis, FH_CVD_broth, FH_CVD_comp, FH_CVD_mat,
FH_CVD_parent, FH_CVD_pat, FH_CVD_sibling, FH_CVD_sis, FH_CVdeath_broth, FH_CVdeath_comp, FH_CVdeath_mat, FH_CVdeath_parent, FH_CVdeath_pat, FH_CVdeath_sibling,
FH_CVdeath_sis, FH_DM_broth, FH_DM_comp, FH_DM_mat, FH_DM_parent, FH_DM_pat, FH_DM_sibling, FH_DM_sis, FH_HC_broth, FH_HC_comp, FH_HC_mat, FH_HC_parent, FH_HC_pat,
FH_HC_sibling, FH_HC_sis, FH_HT_broth, FH_HT_comp, FH_HT_mat, FH_HT_parent, FH_HT_pat, FH_HT_sibling, FH_HT_sis, FH_MI_broth, FH_MI_comp, FH_MI_mat, FH_MI_parent,
FH_MI_pat, FH_MI_sibling, FH_MI_sis, FH_otherCVD_broth, FH_otherCVD_comp, FH_otherCVD_mat, FH_otherCVD_parent, FH_otherCVD_pat, FH_otherCVD_sibling,
FH_otherCVD_sis, FH_PAD_broth, FH_PAD_comp, FH_PAD_mat, FH_PAD_parent, FH_PAD_pat, FH_PAD_sibling, FH_PAD_sis, FH_PAV_broth, FH_PAV_comp, FH_PAV_mat, FH_PAV_parent,
FH_PAV_pat, FH_PAV_sibling, FH_PAV_sis, FH_POB_broth, FH_POB_comp, FH_POB_mat, FH_POB_parent, FH_POB_pat, FH_POB_sibling, FH_POB_sis, FH_risk_broth, FH_risk_comp,
FH_risk_mat, FH_risk_parent, FH_risk_pat, FH_risk_sibling, FH_risk_sis, FH_Stroke_broth, FH_Stroke_comp, FH_Stroke_mat, FH_Stroke_parent, FH_Stroke_pat,
FH_Stroke_sibling, FH_Stroke_sis, FH_tromb_broth, FH_tromb_comp, FH_tromb_mat, FH_tromb_parent, FH_tromb_pat, FH_tromb_sibling, FH_tromb_sis, filter_$, folicaci,
followup1, followup2, followup3, Fontaine, FU_check, FU_check_date, FU.cutt.off.30days, FU.cutt.off.3years, FU1JAAR, FU2JAAR, FU3JAAR, FURIN_low, FURIN_up,
GDF15_plasma, geen_med, Gender, GFR_CG, GFR_MDRD, glucose, GR_Segment, GrB_plaque, GrB_serum, grel, GrK_plaque, GrK_serum, GrM_plaque, GrM_serum, HA, hb, HDAC9,
HDL, HDL_2016, HDL_all, HDL_avg, HDL_clinic, HDL_dif, HDL_final, HDL_finalCU, hdl_source, HDL_var, heart300, heart301, heart302, heart303, heart304, heart305,
heart306, heart307, heart308, heart309, heart310, heart311, heart312, heart313, heart314, heart315, heart316, heart317, heart318, heart319, heart320, heart321,
heart322, heart323, heart324, heart325, heart326, heart327, heart328, HIF1A, ho1, homocys, Hospital, hrt31301, hsCRP_plasma, ht, HYAL55KD, HYALURON,
Hypertension.composite, Hypertension.drugs, Hypertension1, Hypertension2, IL1_Beta, IL10, IL12, IL13, IL17, IL2, IL21, IL4, IL5, IL6, IL6_pg_ug_2015,
IL6R_pg_ug_2015, IL8, IL8_pg_ug_2015, IL9, indexsymptoms_latest, indexsymptoms_latest_4g, indexsymptoms_worst, indexsymptoms_worst_4g, INFG, informedconsent,
insulin, insuline, INVULDAT, IP10, IPH_extended.bin, IPH.bin, ironfoli, ironfoli2, KDOQI, latest, LDL, LDL_2016, LDL_all, LDL_avg, LDL_clinic, LDL_dif, LDL_final,
LDL_finalCU, ldl_source, LDL_var, leg501, leg502, leg503, leg504, leg505, leg506, leg507, leg508, leg509, leg510, leg511, leg512, leg513, leg514, leg515, leg516,
leg517, leg518, leg519, leg520, LMW1STME, LTB4, LTB4R, macmean0, macrophages, macrophages_location, Macrophages.bin, MAP, Mast_cells_plaque, max.followup, MCP1,
MCP1_pg_ug_2015, MCSF_pg_ug_2015, MDC, Med_notes, Med.ablock, Med.ACE_inh, Med.acetylsal, Med.acetylsal_Combi1, Med.acetylsal_Combi2, Med.acetylsal_Combi3,
Med.ADPinh, Med.all.antiplatelet, Med.angiot2.antag, Med.antiarrh, Med.anticoagulants, Med.ascal, Med.aspirin.derived, Med.bblocker, Med.calc_antag,
Med.dipyridamole, Med.diuretic, Med.LLD, Med.nitrate, Med.otheranthyp, Med.renin, Med.statin, Med.statin.derived, Med.Statin.LLD, Med.statin2, media, MG_H1, MI_Dx,
MI_Dx1, MI_Dx2, MIF, MIG, MIP1a, miRNA100_RNU19, miRNA100_RNU48, miRNA155_RNU19, miRNA155_RNU48, MMP14, MMP2, MMP2TIMP2, MMP8, MMP9, MMP9TIMP1, MPO_plasma, MRP_14,
MRP_8, MRP_8_14C, MRP_8_14C_buhlmann, MRP14_plasma, MRP8_14C_plasma, MRP8_plasma, negatibl, neuropsy, neuropsy2, neuropsy3, neuropsy4, neurpsy5, neutrophils, NGAL,
NGAL_low, NGAL_MMP9_complex, NGAL_MMP9_local, NGAL_MMP9_peripheral, NGAL_total, NGAL_up, nitrate, nitrate2, NOD1, NOD2, nogobt1_recalculated, NTproBNP_plasma,
Number_Events_Sorter, Number_Sorted_CD14, Number_Sorted_CD20, Number_Sorted_CD4_Cells, Number_Sorted_CD8_Cells, oac701, oac702, oac70305, oac704, oac705, oac706,
oac707, oac708, oac709, oac710, oac711, oac712, oac713, oac714, OKyear, OPG, OPG_plasma, OPN, OPN_2013, OPN_plasma, OR_blood, Oral.glucose.inh, oralgluc, oralgluc2,
oralgluc3, oralgluc4, ORyear, othanthyp, othcoron, other, other2, OverallPlaquePhenotype, PAI1_pg_ug_2015, PAOD, PARC, patch, PCSK9_plasma, PDGF_BB_plasma,
Percentage_CD14, Percentage_CD20, Percentage_CD4, Percentage_CD8, Peripheral.interv, PKC, PLA2_plasma, plaquephenotype, positibl, PrimaryLast, PrimaryLast1,
prostagl, PulsePressure, qual01, qual02, qual0301, qual0302, qual0303, qual0304, qual0305, qual0306, qual0307, qual0308, qual0309, qual0310, qual0401, qual0402,
qual0403, qual0404, qual0501, qual0502, qual0503, qual06, qual07, qual08, qual0901, qual0902, qual0903, qual0904, qual0905, qual0906, qual0907, qual0908, qual0909,
qual1010, qual1101, qual1102, qual1103, qual1104, RAAS_med, RANTES, RANTES_pg_ug_2015, RANTES_plasma, Ras, RE50_01, RE70_01, Renine_recode, renineinh, restenos,
restenosisOK, rheuma, rheuma2, rheuma3, risk601, risk602, risk603, risk604, risk605, risk606, risk607, risk608, risk609, risk610, risk611, risk612, risk613,
risk614, risk615, risk616, risk617, risk618, risk619, risk620, SHBG, sICAM1, SMAD1_5_8, SMAD2, SMAD3, smc, smc_location, smc_macrophages_ratio, SMC.bin, smcmean0,
SmokerCurrent, SmokingReported, SmokingYearOR, stat3P, statin2, statines, ste3mext, sten1yr, sten3mo, stenose, stenosis_con_bin, Stenosis_contralateral,
Stenosis_ipsilateral, Stroke_Dx, Stroke_eitherside, Stroke_history, Stroke_Symptoms, StrokeTIA_Dx, StrokeTIA_history, StrokeTIA_Symptoms, STUDY_NUMBER, sympt,
Sympt_latest, Sympt_worst, sympt1, sympt2, sympt3, sympt4, Symptoms.3g, Symptoms.4g, Symptoms.5G, systolic, T_NUMBER, TARC, TAT_plasma, TC_2016, TC_all, TC_avg,
TC_clinic, TC_dif, TC_final, TC_finalCU, TC_var, Testosterone, TG_2016, TG_all, TG_avg, TG_clinic, TG_dif, TG_final, TG_finalCU, TG_var, TGF, TGFB, thrombos,
thrombus, thrombus_location, thrombus_new, thrombus_organization, thrombus_organization_v2, thrombus_percentage, thyros2, thyrosta, Time_event_OR, TimeOR_latest,
TimeOR_latest_4g, TimeOR_worst, TimeOR_worst_4g, TIMP1, TIMP2, TISNOW, TNFA, totalchol, totalcholesterol_source, tractdig, tractdig2, tractdig3, tractdig4,
tractres, Treatment.DM, TREM1, triglyceride_source, triglyceriden, Trop1, Trop1DT, Trop2, Trop2DT, Trop3, Trop3DT, TropmaxpostOK, TropoMax, TropoMaxDT,
tropomaxpositief, TSratio_blood, TSratio_plaque, UPID, validation_date, validation1, validation2, validation3, validation4, validation5, validation6, VAR00001,
VEFGA, VEGFA_plasma, vegfa422, vessel_density, vessel_density_additional, vessel_density_averaged, vessel_density_Timo2012, vessel_density_Timo2012_2,
vessel_density_Timo2013, vitamin, vitamin2, vitb12, VRAGENLIJST, vWF_plasma, WBC_THAW, Which.femoral.artery, Whichoperation, writtenIC, yearablo, yearablo2,
yearablo3, yearace, yearace2, yearacet, yearanal, yearanal2, yearanal3, yearangi, yearanta, yearanta2, yearanti, yearanti2, yearbblo, yearbblo2, yearcalc,
yearcalc2, yearcalreg, yearcarb, yearchol, yearchol2, yearchol3, yearclau1, yearclau2, yearclop, yearcom1, yearcom2, yearcom3, yearcort, yearcorthorm2, yearderm,
yeardig, yeardig2, yeardig3, yeardig4, yeardipy, yeardiur, yeardiur2, yeardiur3, yearerec, yeareye, yeargluc, yeargluc2, yeargluc3, yeargluc4, yeargrel, yearinsu,
yeariron, yeariron2, yearneur, yearneur2, yearneur3, yearneur4, yearnitr, yearnitr2, yearOR_bin_2010, YearOR_per2years, yearotant, yearotcor, yearoth2, yearothe,
yearpros, yearpsy5, yearren, yearresp, yearrheu, yearrheu2, yearrheu3, yearsta2, yearstat, yearthro, yearthyr, yearthyr2, yearvit2, yearvita, Yrs.no.smoking,
Yrs.smoking
The following objects are masked from AEDB (pos = 7):
ABI_70, ABI_max, ABI_mean, ABI_min, ABI_OP, ablock, ablock2, ablock3, aceinhib, aceinhib2, acetylsa, Adiponectin_pg_ug_2015, AE_AAA_bijzonderheden, Age, Age_Q,
AgeSQR, aid, Aldosteron_recode, alg10201, alg10202, alg10203, alg10204, alg10205, alg105, alg106, alg109, alg110, alg113, alg114, alg115, ALOX5, analg2, analg3,
analgeti, Ang2, angioii, ANGPT2, anti_apoA1_IgG, anti_apoA1_index, anti_apoA1_na, antiall, antiall2, antiarrh, antiarrh2, ANXA2, AP_Dx, AP_Dx1, AP_Dx2, APOB,
artercon, Artery_summary, arteryop, AsymptSympt, AsymptSympt2G, bblock, bblock2, blocko, blocksnr, BMI, BMI_US, BMI_WHO, BMI30ormore, brain401, brain402, brain403,
brain404, brain405, brain406, brain407, brain408, brain409, brain410, brain411, brain412, brain413, brn40701, bspoed, CAD_Dx, CAD_Dx1, CAD_Dx2, CAD_history,
CADPAOD_history, Calc.bin, calcification, calcium, calcium2, calreg, carbasal, cardioembolic, Caspase3_7, CAV1, CD44, CD44V3, CEA_or_CAS, CEL, CFD_recalc, cholverl,
cholverl2, cholverl3, CI_history, clau1, clau2, Claudication, clopidog, CML, collagen, Collagen.bin, combi1, combi2, combi3, comorbidity.DM, concablo, concablo2,
concablo3, concace2, concacei, concacet, concalle, concanal, concanal2, concanal3, concangi, concanta2, concanti, concanti2, concbblo, concbblo2, conccalc,
conccalc2, conccalreg, conccarb, concchol, concchol2, concchol3, concclau1, concclau2, concclop, conccom1, conccom2, conccom3, conccort, conccorthorm2, concderm,
concdig, concdig2, concdig3, concdig4, concdipy, concdiur, concdiur2, concdiur3, concerec, conceye, concgluc, concgluc2, concgluc3, concgluc4, concgrel, concinsu,
conciron, conciron2, concneur, concneur2, concneur3, concneur4, concnitr, concnitr2, concotant, concotcor, concoth2, concothe, concpros, concpsy5, concren,
concresp, concrheu, concrheu2, concrheu3, concsta2, concstat, concthro, concthyr, concthyr2, concvit2, concvita, Contralateral_surgery, conwhen, corticos,
cortihorm2, creat, crp_all, CRP_avg, CRP_dif, crp_source, CRP_var, CST3_pg_ug, CST3_serum_luminex, CTGF, cTNI_plasma, CTSA, CTSB, CTSL1, CTSS, cyr61,
date_ic_patient, date_ic_researcher, Date.of.birth, date.previous.operation, date1yr, date3mon, dateapprox_latest, dateapprox_worst, dateapprox1, dateapprox2,
dateapprox3, dateapprox4, dateend1, dateend2, dateend3, dateend4, dateend5, dateend6, dateexact_latest, dateexact_worst, dateexact1, dateexact2, dateexact3,
dateexact4, dateok, dermacor, DiabetesStatus, diastoli, diet801, diet802, diet803, diet804, diet805, diet806, diet807, diet808, diet809, diet810, diet811, diet812,
diet813, diet814, diet815, diet816, diet817, diet818, diet819, diet820, diet821, diet822, diet823, diet824, dipyridi, diuretic, diuretic2, diuretic3, DM,
DM.composite, duaalantiplatelet, duplend, eaindexl, eaindexr, eCigarettes, edaplaqu_recalc, edavrspl, EGR, EMMPRIN_45kD, EMMPRIN_58kD, ENDOGLIN, endpoint1,
endpoint2, endpoint3, endpoint4, endpoint5, endpoint6, Eotaxin1, EP_CAD, ep_cad_t_30days, ep_cad_t_3years, EP_CAD_time, ep_cad.30days, EP_CI, ep_ci_t_30days,
ep_ci_t_3years, EP_CI_time, ep_com_t_30days, ep_com_t_3years, EP_composite, EP_composite_time, EP_coronary, ep_coronary_t_3years, EP_coronary_time, EP_CVdeath,
ep_cvdeath_t_30days, ep_cvdeath_t_3years, EP_CVdeath_time, EP_death, ep_death_t_30days, ep_death_t_3years, EP_death_time, EP_fatalCVA, ep_fatalCVA_t_30days,
ep_fatalCVA_t_3years, EP_fatalCVA_time, EP_hemorrhagic_stroke, ep_hemorrhagic_stroke_t_3years, EP_hemorrhagic_stroke_time, ep_hemorrhagic_stroke.3years,
EP_ischemic_stroke, ep_ischemic_stroke_t_3years, EP_ischemic_stroke_time, ep_ischemic_stroke.3years, EP_leg_amputation, EP_leg_amputation_time,
ep_legamputation_t_30days, ep_legamputation_t_3years, EP_major, ep_major_t_30days, ep_major_t_3years, EP_major_time, EP_MI, ep_mi_t_30days, ep_mi_t_3years,
EP_MI_time, EP_nonstroke_event, EP_nonstroke_event_time, ep_nonstroke_t_3years, EP_peripheral, ep_peripheral_t_30days, ep_peripheral_t_3years, EP_peripheral_time,
EP_pta, ep_pta_t_30days, ep_pta_t_3years, EP_pta_time, EP_stroke, ep_stroke_t_30days, ep_stroke_t_3years, EP_stroke_time, EP_strokeCVdeath,
ep_strokeCVdeath_t_30days, ep_strokeCVdeath_t_3years, EP_strokeCVdeath_time, EP_strokedeath, ep_strokedeath_t_30days, ep_strokedeath_t_3years, EP_strokedeath_time,
ePackYearsSmoking, epcad.3years, epci.30days, epci.3years, epcom.30days, epcom.3years, epcoronary.3years, epcvdeath.30days, epcvdeath.3years, epdeath.30days,
epdeath.3years, epfatalCVA.30days, epfatalCVA.3years, eplegamputation.30days, eplegamputation.3years, epmajor.30days, epmajor.3years, epmi.30days, epmi.3years,
epnonstroke.3years, epperipheral.30days, epperipheral.3years, eppta.30days, eppta.3years, epstroke.30days, epstroke.3years, epstrokeCVdeath.30days,
epstrokeCVdeath.3years, epstrokedeath.30days, epstrokedeath.3years, erec, Estradiol, everstroke_composite, Everstroke_Ipsilateral, exer901, exer902, exer903,
exer904, exer905, exer906, exer9071, exer9072, exer9073, exer9074, exer9075, exer9076, exer908, exer909, exer910, eyedrop, EZis, FABP_serum, FABP4, FABP4_pg_ug,
FABP4_serum_luminex, fat, Fat.bin_10, Fat.bin_40, Femoral.interv, FH_AAA_broth, FH_AAA_comp, FH_AAA_mat, FH_AAA_parent, FH_AAA_pat, FH_AAA_sibling, FH_AAA_sis,
FH_amp_broth, FH_amp_comp, FH_amp_mat, FH_amp_parent, FH_amp_pat, FH_amp_sibling, FH_amp_sis, FH_CAD_broth, FH_CAD_comp, FH_CAD_mat, FH_CAD_parent, FH_CAD_pat,
FH_CAD_sibling, FH_CAD_sis, FH_corcalc_broth, FH_corcalc_comp, FH_corcalc_mat, FH_corcalc_parent, FH_corcalc_pat, FH_corcalc_sibling, FH_corcalc_sis, FH_CVD_broth,
FH_CVD_comp, FH_CVD_mat, FH_CVD_parent, FH_CVD_pat, FH_CVD_sibling, FH_CVD_sis, FH_CVdeath_broth, FH_CVdeath_comp, FH_CVdeath_mat, FH_CVdeath_parent,
FH_CVdeath_pat, FH_CVdeath_sibling, FH_CVdeath_sis, FH_DM_broth, FH_DM_comp, FH_DM_mat, FH_DM_parent, FH_DM_pat, FH_DM_sibling, FH_DM_sis, FH_HC_broth, FH_HC_comp,
FH_HC_mat, FH_HC_parent, FH_HC_pat, FH_HC_sibling, FH_HC_sis, FH_HT_broth, FH_HT_comp, FH_HT_mat, FH_HT_parent, FH_HT_pat, FH_HT_sibling, FH_HT_sis, FH_MI_broth,
FH_MI_comp, FH_MI_mat, FH_MI_parent, FH_MI_pat, FH_MI_sibling, FH_MI_sis, FH_otherCVD_broth, FH_otherCVD_comp, FH_otherCVD_mat, FH_otherCVD_parent, FH_otherCVD_pat,
FH_otherCVD_sibling, FH_otherCVD_sis, FH_PAD_broth, FH_PAD_comp, FH_PAD_mat, FH_PAD_parent, FH_PAD_pat, FH_PAD_sibling, FH_PAD_sis, FH_PAV_broth, FH_PAV_comp,
FH_PAV_mat, FH_PAV_parent, FH_PAV_pat, FH_PAV_sibling, FH_PAV_sis, FH_POB_broth, FH_POB_comp, FH_POB_mat, FH_POB_parent, FH_POB_pat, FH_POB_sibling, FH_POB_sis,
FH_risk_broth, FH_risk_comp, FH_risk_mat, FH_risk_parent, FH_risk_pat, FH_risk_sibling, FH_risk_sis, FH_Stroke_broth, FH_Stroke_comp, FH_Stroke_mat,
FH_Stroke_parent, FH_Stroke_pat, FH_Stroke_sibling, FH_Stroke_sis, FH_tromb_broth, FH_tromb_comp, FH_tromb_mat, FH_tromb_parent, FH_tromb_pat, FH_tromb_sibling,
FH_tromb_sis, filter_$, folicaci, followup1, followup2, followup3, Fontaine, FU_check, FU_check_date, FU.cutt.off.30days, FU.cutt.off.3years, FU1JAAR, FU2JAAR,
FU3JAAR, FURIN_low, FURIN_up, GDF15_plasma, geen_med, Gender, GFR_CG, GFR_MDRD, glucose, GR_Segment, GrB_plaque, GrB_serum, grel, GrK_plaque, GrK_serum, GrM_plaque,
GrM_serum, HA, hb, HDAC9, HDL, HDL_2016, HDL_all, HDL_avg, HDL_clinic, HDL_dif, HDL_final, HDL_finalCU, hdl_source, HDL_var, heart300, heart301, heart302, heart303,
heart304, heart305, heart306, heart307, heart308, heart309, heart310, heart311, heart312, heart313, heart314, heart315, heart316, heart317, heart318, heart319,
heart320, heart321, heart322, heart323, heart324, heart325, heart326, heart327, heart328, HIF1A, ho1, homocys, Hospital, hrt31301, hsCRP_plasma, ht, HYAL55KD,
HYALURON, Hypertension.composite, Hypertension.drugs, Hypertension1, Hypertension2, IL1_Beta, IL10, IL12, IL13, IL17, IL2, IL21, IL4, IL5, IL6, IL6_pg_ug_2015,
IL6R_pg_ug_2015, IL8, IL8_pg_ug_2015, IL9, indexsymptoms_latest, indexsymptoms_latest_4g, indexsymptoms_worst, indexsymptoms_worst_4g, INFG, informedconsent,
insulin, insuline, INVULDAT, IP10, IPH_extended.bin, IPH.bin, ironfoli, ironfoli2, KDOQI, latest, LDL, LDL_2016, LDL_all, LDL_avg, LDL_clinic, LDL_dif, LDL_final,
LDL_finalCU, ldl_source, LDL_var, leg501, leg502, leg503, leg504, leg505, leg506, leg507, leg508, leg509, leg510, leg511, leg512, leg513, leg514, leg515, leg516,
leg517, leg518, leg519, leg520, LMW1STME, LTB4, LTB4R, macmean0, macrophages, macrophages_location, Macrophages.bin, MAP, Mast_cells_plaque, max.followup, MCP1,
MCP1_pg_ug_2015, MCSF_pg_ug_2015, MDC, Med_notes, Med.ablock, Med.ACE_inh, Med.acetylsal, Med.acetylsal_Combi1, Med.acetylsal_Combi2, Med.acetylsal_Combi3,
Med.ADPinh, Med.all.antiplatelet, Med.angiot2.antag, Med.antiarrh, Med.anticoagulants, Med.ascal, Med.aspirin.derived, Med.bblocker, Med.calc_antag,
Med.dipyridamole, Med.diuretic, Med.LLD, Med.nitrate, Med.otheranthyp, Med.renin, Med.statin, Med.statin.derived, Med.Statin.LLD, Med.statin2, media, MG_H1, MI_Dx,
MI_Dx1, MI_Dx2, MIF, MIG, MIP1a, miRNA100_RNU19, miRNA100_RNU48, miRNA155_RNU19, miRNA155_RNU48, MMP14, MMP2, MMP2TIMP2, MMP8, MMP9, MMP9TIMP1, MPO_plasma, MRP_14,
MRP_8, MRP_8_14C, MRP_8_14C_buhlmann, MRP14_plasma, MRP8_14C_plasma, MRP8_plasma, negatibl, neuropsy, neuropsy2, neuropsy3, neuropsy4, neurpsy5, neutrophils, NGAL,
NGAL_low, NGAL_MMP9_complex, NGAL_MMP9_local, NGAL_MMP9_peripheral, NGAL_total, NGAL_up, nitrate, nitrate2, NOD1, NOD2, nogobt1_recalculated, NTproBNP_plasma,
Number_Events_Sorter, Number_Sorted_CD14, Number_Sorted_CD20, Number_Sorted_CD4_Cells, Number_Sorted_CD8_Cells, oac701, oac702, oac70305, oac704, oac705, oac706,
oac707, oac708, oac709, oac710, oac711, oac712, oac713, oac714, OKyear, OPG, OPG_plasma, OPN, OPN_2013, OPN_plasma, OR_blood, Oral.glucose.inh, oralgluc, oralgluc2,
oralgluc3, oralgluc4, ORyear, othanthyp, othcoron, other, other2, OverallPlaquePhenotype, PAI1_pg_ug_2015, PAOD, PARC, patch, PCSK9_plasma, PDGF_BB_plasma,
Percentage_CD14, Percentage_CD20, Percentage_CD4, Percentage_CD8, Peripheral.interv, PKC, PLA2_plasma, plaquephenotype, positibl, PrimaryLast, PrimaryLast1,
prostagl, PulsePressure, qual01, qual02, qual0301, qual0302, qual0303, qual0304, qual0305, qual0306, qual0307, qual0308, qual0309, qual0310, qual0401, qual0402,
qual0403, qual0404, qual0501, qual0502, qual0503, qual06, qual07, qual08, qual0901, qual0902, qual0903, qual0904, qual0905, qual0906, qual0907, qual0908, qual0909,
qual1010, qual1101, qual1102, qual1103, qual1104, RAAS_med, RANTES, RANTES_pg_ug_2015, RANTES_plasma, Ras, RE50_01, RE70_01, Renine_recode, renineinh, restenos,
restenosisOK, rheuma, rheuma2, rheuma3, risk601, risk602, risk603, risk604, risk605, risk606, risk607, risk608, risk609, risk610, risk611, risk612, risk613,
risk614, risk615, risk616, risk617, risk618, risk619, risk620, SHBG, sICAM1, SMAD1_5_8, SMAD2, SMAD3, smc, smc_location, smc_macrophages_ratio, SMC.bin, smcmean0,
SmokerCurrent, SmokingReported, SmokingYearOR, stat3P, statin2, statines, ste3mext, sten1yr, sten3mo, stenose, stenosis_con_bin, Stenosis_contralateral,
Stenosis_ipsilateral, Stroke_Dx, Stroke_eitherside, Stroke_history, Stroke_Symptoms, StrokeTIA_Dx, StrokeTIA_history, StrokeTIA_Symptoms, STUDY_NUMBER, sympt,
Sympt_latest, Sympt_worst, sympt1, sympt2, sympt3, sympt4, Symptoms.3g, Symptoms.4g, Symptoms.5G, systolic, T_NUMBER, TARC, TAT_plasma, TC_2016, TC_all, TC_avg,
TC_clinic, TC_dif, TC_final, TC_finalCU, TC_var, Testosterone, TG_2016, TG_all, TG_avg, TG_clinic, TG_dif, TG_final, TG_finalCU, TG_var, TGF, TGFB, thrombos,
thrombus, thrombus_location, thrombus_new, thrombus_organization, thrombus_organization_v2, thrombus_percentage, thyros2, thyrosta, Time_event_OR, TimeOR_latest,
TimeOR_latest_4g, TimeOR_worst, TimeOR_worst_4g, TIMP1, TIMP2, TISNOW, TNFA, totalchol, totalcholesterol_source, tractdig, tractdig2, tractdig3, tractdig4,
tractres, Treatment.DM, TREM1, triglyceride_source, triglyceriden, Trop1, Trop1DT, Trop2, Trop2DT, Trop3, Trop3DT, TropmaxpostOK, TropoMax, TropoMaxDT,
tropomaxpositief, TSratio_blood, TSratio_plaque, UPID, validation_date, validation1, validation2, validation3, validation4, validation5, validation6, VAR00001,
VEFGA, VEGFA_plasma, vegfa422, vessel_density, vessel_density_additional, vessel_density_averaged, vessel_density_Timo2012, vessel_density_Timo2012_2,
vessel_density_Timo2013, vitamin, vitamin2, vitb12, VRAGENLIJST, vWF_plasma, WBC_THAW, Which.femoral.artery, Whichoperation, writtenIC, yearablo, yearablo2,
yearablo3, yearace, yearace2, yearacet, yearanal, yearanal2, yearanal3, yearangi, yearanta, yearanta2, yearanti, yearanti2, yearbblo, yearbblo2, yearcalc,
yearcalc2, yearcalreg, yearcarb, yearchol, yearchol2, yearchol3, yearclau1, yearclau2, yearclop, yearcom1, yearcom2, yearcom3, yearcort, yearcorthorm2, yearderm,
yeardig, yeardig2, yeardig3, yeardig4, yeardipy, yeardiur, yeardiur2, yeardiur3, yearerec, yeareye, yeargluc, yeargluc2, yeargluc3, yeargluc4, yeargrel, yearinsu,
yeariron, yeariron2, yearneur, yearneur2, yearneur3, yearneur4, yearnitr, yearnitr2, yearOR_bin_2010, YearOR_per2years, yearotant, yearotcor, yearoth2, yearothe,
yearpros, yearpsy5, yearren, yearresp, yearrheu, yearrheu2, yearrheu3, yearsta2, yearstat, yearthro, yearthyr, yearthyr2, yearvit2, yearvita, Yrs.no.smoking,
Yrs.smoking
The following objects are masked from AEDB (pos = 8):
ABI_70, ABI_max, ABI_mean, ABI_min, ABI_OP, ablock, ablock2, ablock3, aceinhib, aceinhib2, acetylsa, Adiponectin_pg_ug_2015, AE_AAA_bijzonderheden, Age, Age_Q,
AgeSQR, aid, Aldosteron_recode, alg10201, alg10202, alg10203, alg10204, alg10205, alg105, alg106, alg109, alg110, alg113, alg114, alg115, ALOX5, analg2, analg3,
analgeti, Ang2, angioii, ANGPT2, anti_apoA1_IgG, anti_apoA1_index, anti_apoA1_na, antiall, antiall2, antiarrh, antiarrh2, ANXA2, AP_Dx, AP_Dx1, AP_Dx2, APOB,
artercon, Artery_summary, arteryop, AsymptSympt, AsymptSympt2G, bblock, bblock2, blocko, blocksnr, BMI, BMI_US, BMI_WHO, BMI30ormore, brain401, brain402, brain403,
brain404, brain405, brain406, brain407, brain408, brain409, brain410, brain411, brain412, brain413, brn40701, bspoed, CAD_Dx, CAD_Dx1, CAD_Dx2, CAD_history,
CADPAOD_history, Calc.bin, calcification, calcium, calcium2, calreg, carbasal, cardioembolic, Caspase3_7, CAV1, CD44, CD44V3, CEA_or_CAS, CEL, CFD_recalc, cholverl,
cholverl2, cholverl3, CI_history, clau1, clau2, Claudication, clopidog, CML, collagen, Collagen.bin, combi1, combi2, combi3, comorbidity.DM, concablo, concablo2,
concablo3, concace2, concacei, concacet, concalle, concanal, concanal2, concanal3, concangi, concanta2, concanti, concanti2, concbblo, concbblo2, conccalc,
conccalc2, conccalreg, conccarb, concchol, concchol2, concchol3, concclau1, concclau2, concclop, conccom1, conccom2, conccom3, conccort, conccorthorm2, concderm,
concdig, concdig2, concdig3, concdig4, concdipy, concdiur, concdiur2, concdiur3, concerec, conceye, concgluc, concgluc2, concgluc3, concgluc4, concgrel, concinsu,
conciron, conciron2, concneur, concneur2, concneur3, concneur4, concnitr, concnitr2, concotant, concotcor, concoth2, concothe, concpros, concpsy5, concren,
concresp, concrheu, concrheu2, concrheu3, concsta2, concstat, concthro, concthyr, concthyr2, concvit2, concvita, Contralateral_surgery, conwhen, corticos,
cortihorm2, creat, crp_all, CRP_avg, CRP_dif, crp_source, CRP_var, CST3_pg_ug, CST3_serum_luminex, CTGF, cTNI_plasma, CTSA, CTSB, CTSL1, CTSS, cyr61,
date_ic_patient, date_ic_researcher, Date.of.birth, date.previous.operation, date1yr, date3mon, dateapprox_latest, dateapprox_worst, dateapprox1, dateapprox2,
dateapprox3, dateapprox4, dateend1, dateend2, dateend3, dateend4, dateend5, dateend6, dateexact_latest, dateexact_worst, dateexact1, dateexact2, dateexact3,
dateexact4, dateok, dermacor, diastoli, diet801, diet802, diet803, diet804, diet805, diet806, diet807, diet808, diet809, diet810, diet811, diet812, diet813,
diet814, diet815, diet816, diet817, diet818, diet819, diet820, diet821, diet822, diet823, diet824, dipyridi, diuretic, diuretic2, diuretic3, DM, DM.composite,
duaalantiplatelet, duplend, eaindexl, eaindexr, eCigarettes, edaplaqu_recalc, edavrspl, EGR, EMMPRIN_45kD, EMMPRIN_58kD, ENDOGLIN, endpoint1, endpoint2, endpoint3,
endpoint4, endpoint5, endpoint6, Eotaxin1, EP_CAD, ep_cad_t_30days, ep_cad_t_3years, EP_CAD_time, ep_cad.30days, EP_CI, ep_ci_t_30days, ep_ci_t_3years, EP_CI_time,
ep_com_t_30days, ep_com_t_3years, EP_composite, EP_composite_time, EP_coronary, ep_coronary_t_3years, EP_coronary_time, EP_CVdeath, ep_cvdeath_t_30days,
ep_cvdeath_t_3years, EP_CVdeath_time, EP_death, ep_death_t_30days, ep_death_t_3years, EP_death_time, EP_fatalCVA, ep_fatalCVA_t_30days, ep_fatalCVA_t_3years,
EP_fatalCVA_time, EP_hemorrhagic_stroke, ep_hemorrhagic_stroke_t_3years, EP_hemorrhagic_stroke_time, ep_hemorrhagic_stroke.3years, EP_ischemic_stroke,
ep_ischemic_stroke_t_3years, EP_ischemic_stroke_time, ep_ischemic_stroke.3years, EP_leg_amputation, EP_leg_amputation_time, ep_legamputation_t_30days,
ep_legamputation_t_3years, EP_major, ep_major_t_30days, ep_major_t_3years, EP_major_time, EP_MI, ep_mi_t_30days, ep_mi_t_3years, EP_MI_time, EP_nonstroke_event,
EP_nonstroke_event_time, ep_nonstroke_t_3years, EP_peripheral, ep_peripheral_t_30days, ep_peripheral_t_3years, EP_peripheral_time, EP_pta, ep_pta_t_30days,
ep_pta_t_3years, EP_pta_time, EP_stroke, ep_stroke_t_30days, ep_stroke_t_3years, EP_stroke_time, EP_strokeCVdeath, ep_strokeCVdeath_t_30days,
ep_strokeCVdeath_t_3years, EP_strokeCVdeath_time, EP_strokedeath, ep_strokedeath_t_30days, ep_strokedeath_t_3years, EP_strokedeath_time, ePackYearsSmoking,
epcad.3years, epci.30days, epci.3years, epcom.30days, epcom.3years, epcoronary.3years, epcvdeath.30days, epcvdeath.3years, epdeath.30days, epdeath.3years,
epfatalCVA.30days, epfatalCVA.3years, eplegamputation.30days, eplegamputation.3years, epmajor.30days, epmajor.3years, epmi.30days, epmi.3years, epnonstroke.3years,
epperipheral.30days, epperipheral.3years, eppta.30days, eppta.3years, epstroke.30days, epstroke.3years, epstrokeCVdeath.30days, epstrokeCVdeath.3years,
epstrokedeath.30days, epstrokedeath.3years, erec, Estradiol, everstroke_composite, Everstroke_Ipsilateral, exer901, exer902, exer903, exer904, exer905, exer906,
exer9071, exer9072, exer9073, exer9074, exer9075, exer9076, exer908, exer909, exer910, eyedrop, EZis, FABP_serum, FABP4, FABP4_pg_ug, FABP4_serum_luminex, fat,
Fat.bin_10, Fat.bin_40, Femoral.interv, FH_AAA_broth, FH_AAA_comp, FH_AAA_mat, FH_AAA_parent, FH_AAA_pat, FH_AAA_sibling, FH_AAA_sis, FH_amp_broth, FH_amp_comp,
FH_amp_mat, FH_amp_parent, FH_amp_pat, FH_amp_sibling, FH_amp_sis, FH_CAD_broth, FH_CAD_comp, FH_CAD_mat, FH_CAD_parent, FH_CAD_pat, FH_CAD_sibling, FH_CAD_sis,
FH_corcalc_broth, FH_corcalc_comp, FH_corcalc_mat, FH_corcalc_parent, FH_corcalc_pat, FH_corcalc_sibling, FH_corcalc_sis, FH_CVD_broth, FH_CVD_comp, FH_CVD_mat,
FH_CVD_parent, FH_CVD_pat, FH_CVD_sibling, FH_CVD_sis, FH_CVdeath_broth, FH_CVdeath_comp, FH_CVdeath_mat, FH_CVdeath_parent, FH_CVdeath_pat, FH_CVdeath_sibling,
FH_CVdeath_sis, FH_DM_broth, FH_DM_comp, FH_DM_mat, FH_DM_parent, FH_DM_pat, FH_DM_sibling, FH_DM_sis, FH_HC_broth, FH_HC_comp, FH_HC_mat, FH_HC_parent, FH_HC_pat,
FH_HC_sibling, FH_HC_sis, FH_HT_broth, FH_HT_comp, FH_HT_mat, FH_HT_parent, FH_HT_pat, FH_HT_sibling, FH_HT_sis, FH_MI_broth, FH_MI_comp, FH_MI_mat, FH_MI_parent,
FH_MI_pat, FH_MI_sibling, FH_MI_sis, FH_otherCVD_broth, FH_otherCVD_comp, FH_otherCVD_mat, FH_otherCVD_parent, FH_otherCVD_pat, FH_otherCVD_sibling,
FH_otherCVD_sis, FH_PAD_broth, FH_PAD_comp, FH_PAD_mat, FH_PAD_parent, FH_PAD_pat, FH_PAD_sibling, FH_PAD_sis, FH_PAV_broth, FH_PAV_comp, FH_PAV_mat, FH_PAV_parent,
FH_PAV_pat, FH_PAV_sibling, FH_PAV_sis, FH_POB_broth, FH_POB_comp, FH_POB_mat, FH_POB_parent, FH_POB_pat, FH_POB_sibling, FH_POB_sis, FH_risk_broth, FH_risk_comp,
FH_risk_mat, FH_risk_parent, FH_risk_pat, FH_risk_sibling, FH_risk_sis, FH_Stroke_broth, FH_Stroke_comp, FH_Stroke_mat, FH_Stroke_parent, FH_Stroke_pat,
FH_Stroke_sibling, FH_Stroke_sis, FH_tromb_broth, FH_tromb_comp, FH_tromb_mat, FH_tromb_parent, FH_tromb_pat, FH_tromb_sibling, FH_tromb_sis, filter_$, folicaci,
followup1, followup2, followup3, Fontaine, FU_check, FU_check_date, FU.cutt.off.30days, FU.cutt.off.3years, FU1JAAR, FU2JAAR, FU3JAAR, FURIN_low, FURIN_up,
GDF15_plasma, geen_med, Gender, GFR_CG, GFR_MDRD, glucose, GR_Segment, GrB_plaque, GrB_serum, grel, GrK_plaque, GrK_serum, GrM_plaque, GrM_serum, HA, hb, HDAC9,
HDL, HDL_2016, HDL_all, HDL_avg, HDL_clinic, HDL_dif, HDL_final, HDL_finalCU, hdl_source, HDL_var, heart300, heart301, heart302, heart303, heart304, heart305,
heart306, heart307, heart308, heart309, heart310, heart311, heart312, heart313, heart314, heart315, heart316, heart317, heart318, heart319, heart320, heart321,
heart322, heart323, heart324, heart325, heart326, heart327, heart328, HIF1A, ho1, homocys, Hospital, hrt31301, hsCRP_plasma, ht, HYAL55KD, HYALURON,
Hypertension.composite, Hypertension.drugs, Hypertension1, Hypertension2, IL1_Beta, IL10, IL12, IL13, IL17, IL2, IL21, IL4, IL5, IL6, IL6_pg_ug_2015,
IL6R_pg_ug_2015, IL8, IL8_pg_ug_2015, IL9, indexsymptoms_latest, indexsymptoms_latest_4g, indexsymptoms_worst, indexsymptoms_worst_4g, INFG, informedconsent,
insulin, insuline, INVULDAT, IP10, IPH_extended.bin, IPH.bin, ironfoli, ironfoli2, KDOQI, latest, LDL, LDL_2016, LDL_all, LDL_avg, LDL_clinic, LDL_dif, LDL_final,
LDL_finalCU, ldl_source, LDL_var, leg501, leg502, leg503, leg504, leg505, leg506, leg507, leg508, leg509, leg510, leg511, leg512, leg513, leg514, leg515, leg516,
leg517, leg518, leg519, leg520, LMW1STME, LTB4, LTB4R, macmean0, macrophages, macrophages_location, Macrophages.bin, MAP, Mast_cells_plaque, max.followup, MCP1,
MCP1_pg_ug_2015, MCSF_pg_ug_2015, MDC, Med_notes, Med.ablock, Med.ACE_inh, Med.acetylsal, Med.acetylsal_Combi1, Med.acetylsal_Combi2, Med.acetylsal_Combi3,
Med.ADPinh, Med.all.antiplatelet, Med.angiot2.antag, Med.antiarrh, Med.anticoagulants, Med.ascal, Med.aspirin.derived, Med.bblocker, Med.calc_antag,
Med.dipyridamole, Med.diuretic, Med.LLD, Med.nitrate, Med.otheranthyp, Med.renin, Med.statin, Med.statin.derived, Med.Statin.LLD, Med.statin2, media, MG_H1, MI_Dx,
MI_Dx1, MI_Dx2, MIF, MIG, MIP1a, miRNA100_RNU19, miRNA100_RNU48, miRNA155_RNU19, miRNA155_RNU48, MMP14, MMP2, MMP2TIMP2, MMP8, MMP9, MMP9TIMP1, MPO_plasma, MRP_14,
MRP_8, MRP_8_14C, MRP_8_14C_buhlmann, MRP14_plasma, MRP8_14C_plasma, MRP8_plasma, negatibl, neuropsy, neuropsy2, neuropsy3, neuropsy4, neurpsy5, neutrophils, NGAL,
NGAL_low, NGAL_MMP9_complex, NGAL_MMP9_local, NGAL_MMP9_peripheral, NGAL_total, NGAL_up, nitrate, nitrate2, NOD1, NOD2, nogobt1_recalculated, NTproBNP_plasma,
Number_Events_Sorter, Number_Sorted_CD14, Number_Sorted_CD20, Number_Sorted_CD4_Cells, Number_Sorted_CD8_Cells, oac701, oac702, oac70305, oac704, oac705, oac706,
oac707, oac708, oac709, oac710, oac711, oac712, oac713, oac714, OKyear, OPG, OPG_plasma, OPN, OPN_2013, OPN_plasma, OR_blood, Oral.glucose.inh, oralgluc, oralgluc2,
oralgluc3, oralgluc4, ORyear, othanthyp, othcoron, other, other2, OverallPlaquePhenotype, PAI1_pg_ug_2015, PAOD, PARC, patch, PCSK9_plasma, PDGF_BB_plasma,
Percentage_CD14, Percentage_CD20, Percentage_CD4, Percentage_CD8, Peripheral.interv, PKC, PLA2_plasma, plaquephenotype, positibl, PrimaryLast, PrimaryLast1,
prostagl, PulsePressure, qual01, qual02, qual0301, qual0302, qual0303, qual0304, qual0305, qual0306, qual0307, qual0308, qual0309, qual0310, qual0401, qual0402,
qual0403, qual0404, qual0501, qual0502, qual0503, qual06, qual07, qual08, qual0901, qual0902, qual0903, qual0904, qual0905, qual0906, qual0907, qual0908, qual0909,
qual1010, qual1101, qual1102, qual1103, qual1104, RAAS_med, RANTES, RANTES_pg_ug_2015, RANTES_plasma, Ras, RE50_01, RE70_01, Renine_recode, renineinh, restenos,
restenosisOK, rheuma, rheuma2, rheuma3, risk601, risk602, risk603, risk604, risk605, risk606, risk607, risk608, risk609, risk610, risk611, risk612, risk613,
risk614, risk615, risk616, risk617, risk618, risk619, risk620, SHBG, sICAM1, SMAD1_5_8, SMAD2, SMAD3, smc, smc_location, smc_macrophages_ratio, SMC.bin, smcmean0,
SmokerCurrent, SmokingReported, SmokingYearOR, stat3P, statin2, statines, ste3mext, sten1yr, sten3mo, stenose, stenosis_con_bin, Stenosis_contralateral,
Stenosis_ipsilateral, Stroke_Dx, Stroke_eitherside, Stroke_history, Stroke_Symptoms, StrokeTIA_Dx, StrokeTIA_history, StrokeTIA_Symptoms, STUDY_NUMBER, sympt,
Sympt_latest, Sympt_worst, sympt1, sympt2, sympt3, sympt4, Symptoms.3g, Symptoms.4g, Symptoms.5G, systolic, T_NUMBER, TARC, TAT_plasma, TC_2016, TC_all, TC_avg,
TC_clinic, TC_dif, TC_final, TC_finalCU, TC_var, Testosterone, TG_2016, TG_all, TG_avg, TG_clinic, TG_dif, TG_final, TG_finalCU, TG_var, TGF, TGFB, thrombos,
thrombus, thrombus_location, thrombus_new, thrombus_organization, thrombus_organization_v2, thrombus_percentage, thyros2, thyrosta, Time_event_OR, TimeOR_latest,
TimeOR_latest_4g, TimeOR_worst, TimeOR_worst_4g, TIMP1, TIMP2, TISNOW, TNFA, totalchol, totalcholesterol_source, tractdig, tractdig2, tractdig3, tractdig4,
tractres, Treatment.DM, TREM1, triglyceride_source, triglyceriden, Trop1, Trop1DT, Trop2, Trop2DT, Trop3, Trop3DT, TropmaxpostOK, TropoMax, TropoMaxDT,
tropomaxpositief, TSratio_blood, TSratio_plaque, UPID, validation_date, validation1, validation2, validation3, validation4, validation5, validation6, VAR00001,
VEFGA, VEGFA_plasma, vegfa422, vessel_density, vessel_density_additional, vessel_density_averaged, vessel_density_Timo2012, vessel_density_Timo2012_2,
vessel_density_Timo2013, vitamin, vitamin2, vitb12, VRAGENLIJST, vWF_plasma, WBC_THAW, Which.femoral.artery, Whichoperation, writtenIC, yearablo, yearablo2,
yearablo3, yearace, yearace2, yearacet, yearanal, yearanal2, yearanal3, yearangi, yearanta, yearanta2, yearanti, yearanti2, yearbblo, yearbblo2, yearcalc,
yearcalc2, yearcalreg, yearcarb, yearchol, yearchol2, yearchol3, yearclau1, yearclau2, yearclop, yearcom1, yearcom2, yearcom3, yearcort, yearcorthorm2, yearderm,
yeardig, yeardig2, yeardig3, yeardig4, yeardipy, yeardiur, yeardiur2, yeardiur3, yearerec, yeareye, yeargluc, yeargluc2, yeargluc3, yeargluc4, yeargrel, yearinsu,
yeariron, yeariron2, yearneur, yearneur2, yearneur3, yearneur4, yearnitr, yearnitr2, yearOR_bin_2010, YearOR_per2years, yearotant, yearotcor, yearoth2, yearothe,
yearpros, yearpsy5, yearren, yearresp, yearrheu, yearrheu2, yearrheu3, yearsta2, yearstat, yearthro, yearthyr, yearthyr2, yearvit2, yearvita, Yrs.no.smoking,
Yrs.smokingAEDB.CEA[,"epmajor.30days"] <- AEDB.CEA$epmajor.3years
AEDB.CEA$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0
AEDB.CEA[,"epstroke.30days"] <- AEDB.CEA$epstroke.3years
AEDB.CEA$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0
AEDB.CEA[,"epcoronary.30days"] <- AEDB.CEA$epcoronary.3years
AEDB.CEA$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0
AEDB.CEA[,"epcvdeath.30days"] <- AEDB.CEA$epcvdeath.3years
AEDB.CEA$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0
AEDB.CEA[,"epmajor.90days"] <- AEDB.CEA$epmajor.3years
AEDB.CEA$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0
AEDB.CEA[,"epstroke.90days"] <- AEDB.CEA$epstroke.3years
AEDB.CEA$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0
AEDB.CEA[,"epcoronary.90days"] <- AEDB.CEA$epcoronary.3years
AEDB.CEA$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0
AEDB.CEA[,"epcvdeath.90days"] <- AEDB.CEA$epcvdeath.3years
AEDB.CEA$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0
detach(AEDB.CEA)
AEDB.CEA.temp <- subset(AEDB.CEA, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary",
"epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
"epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
"epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)
DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)
rm(AEDB.CEA.temp)
Sanity checks
First we do some sanity checks and inventory the time-to-event and event variables.
# Reference: https://bioconductor.org/packages/devel/bioc/vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html
# If you want to suppress warnings and messages when installing/loading packages
# suppressPackageStartupMessages({})
install.packages.auto("survival")
install.packages.auto("survminer")
install.packages.auto("Hmisc")
cat("* Creating function to summarize Cox regression and prepare container for results.")* Creating function to summarize Cox regression and prepare container for results.# Function to get summary statistics from Cox regression model
COX.STAT <- function(coxfit, DATASET, OUTCOME, protein){
cat("Summarizing Cox regression results for '", protein ,"' and its association to '",OUTCOME,"' in '",DATASET,"'.\n")
if (nrow(summary(coxfit)$coefficients) == 1) {
output = c(protein, rep(NA,8))
cat("Model not fitted; probably singular.\n")
}else {
cat("Collecting data.\n\n")
cox.sum <- summary(coxfit)
cox.effectsize = cox.sum$coefficients[1,1]
cox.SE = cox.sum$coefficients[1,3]
cox.HReffect = cox.sum$coefficients[1,2]
cox.CI_low = exp(cox.effectsize - 1.96 * cox.SE)
cox.CI_up = exp(cox.effectsize + 1.96 * cox.SE)
cox.zvalue = cox.sum$coefficients[1,4]
cox.pvalue = cox.sum$coefficients[1,5]
cox.sample_size = cox.sum$n
cox.nevents = cox.sum$nevent
output = c(DATASET, OUTCOME, protein, cox.effectsize, cox.SE, cox.HReffect, cox.CI_low, cox.CI_up, cox.zvalue, cox.pvalue, cox.sample_size, cox.nevents)
cat("We have collected the following:\n")
cat("Dataset used..............:", DATASET, "\n")
cat("Outcome analyzed..........:", OUTCOME, "\n")
cat("Protein...................:", protein, "\n")
cat("Effect size...............:", round(cox.effectsize, 6), "\n")
cat("Standard error............:", round(cox.SE, 6), "\n")
cat("Odds ratio (effect size)..:", round(cox.HReffect, 3), "\n")
cat("Lower 95% CI..............:", round(cox.CI_low, 3), "\n")
cat("Upper 95% CI..............:", round(cox.CI_up, 3), "\n")
cat("T-value...................:", round(cox.zvalue, 6), "\n")
cat("P-value...................:", signif(cox.pvalue, 8), "\n")
cat("Sample size in model......:", cox.sample_size, "\n")
cat("Number of events..........:", cox.nevents, "\n")
}
return(output)
print(output)
}
times = c("ep_major_t_3years",
"ep_stroke_t_3years", "ep_coronary_t_3years", "ep_cvdeath_t_3years")
endpoints = c("epmajor.3years",
"epstroke.3years", "epcoronary.3years", "epcvdeath.3years")
cat("* Check the cases per event type - for sanity.")* Check the cases per event type - for sanity.for (events in endpoints){
require(labelled)
print(paste0("Printing the summary of: ",events))
# print(summary(AEDB.CEA[,events]))
print(table(AEDB.CEA[,events]))
}[1] "Printing the summary of: epmajor.3years"
0 1
2033 265
[1] "Printing the summary of: epstroke.3years"
0 1
2169 130
[1] "Printing the summary of: epcoronary.3years"
0 1
2117 182
[1] "Printing the summary of: epcvdeath.3years"
0 1
2208 90 cat("* Check distribution of events over time - for sanity.")* Check distribution of events over time - for sanity.for (eventtimes in times){
print(paste0("Printing the summary of: ",eventtimes))
print(summary(AEDB.CEA[,eventtimes]))
}[1] "Printing the summary of: ep_major_t_3years"
ep_major_t_3years
Min. :0.000
1st Qu.:2.708
Median :3.000
Mean :2.573
3rd Qu.:3.000
Max. :3.000
NA's :125
[1] "Printing the summary of: ep_stroke_t_3years"
ep_stroke_t_3years
Min. :0.000
1st Qu.:2.877
Median :3.000
Mean :2.623
3rd Qu.:3.000
Max. :3.000
NA's :125
[1] "Printing the summary of: ep_coronary_t_3years"
ep_coronary_t_3years
Min. :0.000
1st Qu.:2.783
Median :3.000
Mean :2.622
3rd Qu.:3.000
Max. :3.000
NA's :125
[1] "Printing the summary of: ep_cvdeath_t_3years"
ep_cvdeath_t_3years
Min. :0.00274
1st Qu.:2.91233
Median :3.00000
Mean :2.70878
3rd Qu.:3.00000
Max. :3.00000
NA's :125 for (eventtime in times){
print(paste0("Printing the distribution of: ",eventtime))
p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
main = eventtime, bins = 15,
xlab = "year", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal())
print(p)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.EventDistributionPerYear.",eventtime,".pdf"), plot = last_plot())
}[1] "Printing the distribution of: ep_major_t_3years"
[1] "Printing the distribution of: ep_stroke_t_3years"
[1] "Printing the distribution of: ep_coronary_t_3years"
[1] "Printing the distribution of: ep_cvdeath_t_3years"
times30 = c("ep_major_t_30days",
"ep_stroke_t_30days", "ep_coronary_t_30days", "ep_cvdeath_t_30days")
endpoints30 = c("epmajor.30days",
"epstroke.30days", "epcoronary.30days", "epcvdeath.30days")
cat("* Check the cases per event type - for sanity.")* Check the cases per event type - for sanity.for (events in endpoints30){
print(paste0("Printing the summary of: ",events))
# print(summary(AEDB.CEA[,events]))
print(table(AEDB.CEA[,events]))
}[1] "Printing the summary of: epmajor.30days"
0 1
2220 78
[1] "Printing the summary of: epstroke.30days"
0 1
2246 53
[1] "Printing the summary of: epcoronary.30days"
0 1
2265 34
[1] "Printing the summary of: epcvdeath.30days"
0 1
2286 12 cat("* Check distribution of events over time - for sanity.")* Check distribution of events over time - for sanity.for (eventtimes in times30){
print(paste0("Printing the summary of: ",eventtimes))
print(summary(AEDB.CEA[,eventtimes]))
}[1] "Printing the summary of: ep_major_t_30days"
ep_major_t_30days
Min. : 0.00
1st Qu.:30.00
Median :30.00
Mean :29.09
3rd Qu.:30.00
Max. :30.00
NA's :125
[1] "Printing the summary of: ep_stroke_t_30days"
ep_stroke_t_30days
Min. : 0.00
1st Qu.:30.00
Median :30.00
Mean :29.32
3rd Qu.:30.00
Max. :30.00
NA's :125
[1] "Printing the summary of: ep_coronary_t_30days"
ep_coronary_t_30days
Min. : 0.00
1st Qu.:30.00
Median :30.00
Mean :29.54
3rd Qu.:30.00
Max. :30.00
NA's :125
[1] "Printing the summary of: ep_cvdeath_t_30days"
ep_cvdeath_t_30days
Min. : 1.001
1st Qu.:30.000
Median :30.000
Mean :29.854
3rd Qu.:30.000
Max. :30.000
NA's :125 for (eventtime in times30){
print(paste0("Printing the distribution of: ",eventtime))
p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
main = eventtime, bins = 15,
xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal())
print(p)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.EventDistributionPer30Days.",eventtime,".pdf"), plot = last_plot())
}[1] "Printing the distribution of: ep_major_t_30days"
[1] "Printing the distribution of: ep_stroke_t_30days"
[1] "Printing the distribution of: ep_coronary_t_30days"
[1] "Printing the distribution of: ep_cvdeath_t_30days"
times90 = c("ep_major_t_90days",
"ep_stroke_t_90days", "ep_coronary_t_90days", "ep_cvdeath_t_90days")
endpoints90 = c("epmajor.90days",
"epstroke.90days", "epcoronary.90days", "epcvdeath.90days")
cat("* Check the cases per event type - for sanity.")* Check the cases per event type - for sanity.for (events in endpoints90){
print(paste0("Printing the summary of: ",events))
# print(summary(AEDB.CEA[,events]))
print(table(AEDB.CEA[,events]))
}[1] "Printing the summary of: epmajor.90days"
0 1
2204 94
[1] "Printing the summary of: epstroke.90days"
0 1
2239 60
[1] "Printing the summary of: epcoronary.90days"
0 1
2255 44
[1] "Printing the summary of: epcvdeath.90days"
0 1
2279 19 cat("* Check distribution of events over time - for sanity.")* Check distribution of events over time - for sanity.for (eventtimes in times90){
print(paste0("Printing the summary of: ",eventtimes))
print(summary(AEDB.CEA[,eventtimes]))
}[1] "Printing the summary of: ep_major_t_90days"
ep_major_t_90days
Min. : 0.00
1st Qu.:90.00
Median :90.00
Mean :86.74
3rd Qu.:90.00
Max. :90.00
NA's :125
[1] "Printing the summary of: ep_stroke_t_90days"
ep_stroke_t_90days
Min. : 0.00
1st Qu.:90.00
Median :90.00
Mean :87.51
3rd Qu.:90.00
Max. :90.00
NA's :125
[1] "Printing the summary of: ep_coronary_t_90days"
ep_coronary_t_90days
Min. : 0.0
1st Qu.:90.0
Median :90.0
Mean :88.2
3rd Qu.:90.0
Max. :90.0
NA's :125
[1] "Printing the summary of: ep_cvdeath_t_90days"
ep_cvdeath_t_90days
Min. : 1.001
1st Qu.:90.000
Median :90.000
Mean :89.320
3rd Qu.:90.000
Max. :90.000
NA's :125 for (eventtime in times90){
print(paste0("Printing the distribution of: ",eventtime))
p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
main = eventtime, bins = 15,
xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal())
print(p)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.EventDistributionPer90Days.",eventtime,".pdf"), plot = last_plot())
}[1] "Printing the distribution of: ep_major_t_90days"
[1] "Printing the distribution of: ep_stroke_t_90days"
[1] "Printing the distribution of: ep_coronary_t_90days"
[1] "Printing the distribution of: ep_cvdeath_t_90days"
Cox regressions
Let’s perform the actual Cox-regressions. We will apply a couple of models:
- Model 1: adjusted for age and sex
- Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
3 years follow-up
MODEL 1
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
eptime = times[i]
ep = endpoints[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 1 (Simple model)
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
# ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [years]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL1.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}* Analyzing the effect of plaque proteins on [epmajor.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ug_2015_rank]; 1 out of 2 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34055,0.00105) [ 0.00105,3.34055]
598 598
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender, data = TEMP.DF)
n= 1184, number of events= 139
(1237 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 0.037093 1.037789 0.169988 0.218 0.827268
Age 0.033469 1.034035 0.009876 3.389 0.000702 ***
Gendermale 0.336307 1.399769 0.199676 1.684 0.092131 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 1.038 0.9636 0.7437 1.448
Age 1.034 0.9671 1.0142 1.054
Gendermale 1.400 0.7144 0.9464 2.070
Concordance= 0.588 (se = 0.025 )
Likelihood ratio test= 15.11 on 3 df, p=0.002
Wald test = 14.36 on 3 df, p=0.002
Score (logrank) test = 14.43 on 3 df, p=0.002
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: 0.037093
Standard error............: 0.169988
Odds ratio (effect size)..: 1.038
Lower 95% CI..............: 0.744
Upper 95% CI..............: 1.448
T-value...................: 0.218206
P-value...................: 0.8272683
Sample size in model......: 1184
Number of events..........: 139
> processing [MCP1_rank]; 2 out of 2 proteins.
> cross tabulation of MCP1_rank-stratum.
[-2.41053,0.00444) [ 0.00444,3.12635]
283 282
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender, data = TEMP.DF)
n= 558, number of events= 72
(1863 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] -0.20048 0.81834 0.23746 -0.844 0.3985
Age 0.03070 1.03117 0.01446 2.123 0.0338 *
Gendermale 0.81021 2.24838 0.32813 2.469 0.0135 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 0.8183 1.2220 0.5138 1.303
Age 1.0312 0.9698 1.0024 1.061
Gendermale 2.2484 0.4448 1.1819 4.277
Concordance= 0.62 (se = 0.035 )
Likelihood ratio test= 12.85 on 3 df, p=0.005
Wald test = 11.56 on 3 df, p=0.009
Score (logrank) test = 11.89 on 3 df, p=0.008
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: MCP1_rank
Effect size...............: -0.200476
Standard error............: 0.237458
Odds ratio (effect size)..: 0.818
Lower 95% CI..............: 0.514
Upper 95% CI..............: 1.303
T-value...................: -0.844258
P-value...................: 0.3985254
Sample size in model......: 558
Number of events..........: 72
* Analyzing the effect of plaque proteins on [epstroke.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ug_2015_rank]; 1 out of 2 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34055,0.00105) [ 0.00105,3.34055]
598 598
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender, data = TEMP.DF)
n= 1184, number of events= 73
(1237 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 0.13646 1.14621 0.23508 0.580 0.5616
Age 0.03440 1.03500 0.01356 2.537 0.0112 *
Gendermale 0.06281 1.06483 0.25901 0.243 0.8084
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 1.146 0.8724 0.7230 1.817
Age 1.035 0.9662 1.0079 1.063
Gendermale 1.065 0.9391 0.6409 1.769
Concordance= 0.597 (se = 0.033 )
Likelihood ratio test= 7.12 on 3 df, p=0.07
Wald test = 6.89 on 3 df, p=0.08
Score (logrank) test = 6.92 on 3 df, p=0.07
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: 0.136457
Standard error............: 0.235075
Odds ratio (effect size)..: 1.146
Lower 95% CI..............: 0.723
Upper 95% CI..............: 1.817
T-value...................: 0.580483
P-value...................: 0.5615888
Sample size in model......: 1184
Number of events..........: 73
> processing [MCP1_rank]; 2 out of 2 proteins.
> cross tabulation of MCP1_rank-stratum.
[-2.41053,0.00444) [ 0.00444,3.12635]
283 282
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender, data = TEMP.DF)
n= 558, number of events= 38
(1863 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] -0.33054 0.71854 0.32977 -1.002 0.316
Age 0.01603 1.01615 0.01930 0.830 0.406
Gendermale 0.26127 1.29858 0.38266 0.683 0.495
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 0.7185 1.3917 0.3765 1.371
Age 1.0162 0.9841 0.9784 1.055
Gendermale 1.2986 0.7701 0.6134 2.749
Concordance= 0.558 (se = 0.044 )
Likelihood ratio test= 2.22 on 3 df, p=0.5
Wald test = 2.2 on 3 df, p=0.5
Score (logrank) test = 2.21 on 3 df, p=0.5
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: MCP1_rank
Effect size...............: -0.330541
Standard error............: 0.329767
Odds ratio (effect size)..: 0.719
Lower 95% CI..............: 0.376
Upper 95% CI..............: 1.371
T-value...................: -1.002346
P-value...................: 0.3161763
Sample size in model......: 558
Number of events..........: 38
* Analyzing the effect of plaque proteins on [epcoronary.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ug_2015_rank]; 1 out of 2 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34055,0.00105) [ 0.00105,3.34055]
598 598
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender, data = TEMP.DF)
n= 1184, number of events= 91
(1237 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] -0.174454 0.839916 0.210481 -0.829 0.4072
Age 0.007135 1.007160 0.011848 0.602 0.5471
Gendermale 0.666235 1.946893 0.269214 2.475 0.0133 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 0.8399 1.1906 0.556 1.269
Age 1.0072 0.9929 0.984 1.031
Gendermale 1.9469 0.5136 1.149 3.300
Concordance= 0.577 (se = 0.03 )
Likelihood ratio test= 7.93 on 3 df, p=0.05
Wald test = 7.07 on 3 df, p=0.07
Score (logrank) test = 7.29 on 3 df, p=0.06
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: -0.174454
Standard error............: 0.210481
Odds ratio (effect size)..: 0.84
Lower 95% CI..............: 0.556
Upper 95% CI..............: 1.269
T-value...................: -0.828833
P-value...................: 0.4071991
Sample size in model......: 1184
Number of events..........: 91
> processing [MCP1_rank]; 2 out of 2 proteins.
> cross tabulation of MCP1_rank-stratum.
[-2.41053,0.00444) [ 0.00444,3.12635]
283 282
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender, data = TEMP.DF)
n= 558, number of events= 47
(1863 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 0.30417 1.35550 0.29625 1.027 0.3045
Age 0.03606 1.03672 0.01811 1.991 0.0464 *
Gendermale 0.79302 2.21007 0.41021 1.933 0.0532 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 1.356 0.7377 0.7585 2.423
Age 1.037 0.9646 1.0006 1.074
Gendermale 2.210 0.4525 0.9891 4.938
Concordance= 0.628 (se = 0.039 )
Likelihood ratio test= 9.85 on 3 df, p=0.02
Wald test = 8.75 on 3 df, p=0.03
Score (logrank) test = 8.99 on 3 df, p=0.03
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: MCP1_rank
Effect size...............: 0.304172
Standard error............: 0.296253
Odds ratio (effect size)..: 1.356
Lower 95% CI..............: 0.758
Upper 95% CI..............: 2.423
T-value...................: 1.026729
P-value...................: 0.3045479
Sample size in model......: 558
Number of events..........: 47
* Analyzing the effect of plaque proteins on [epcvdeath.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ug_2015_rank]; 1 out of 2 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34055,0.00105) [ 0.00105,3.34055]
598 598
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender, data = TEMP.DF)
n= 1184, number of events= 45
(1237 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] -0.11090 0.89502 0.29877 -0.371 0.7105
Age 0.08444 1.08811 0.01930 4.375 1.21e-05 ***
Gendermale 0.89483 2.44691 0.41200 2.172 0.0299 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 0.895 1.1173 0.4983 1.608
Age 1.088 0.9190 1.0477 1.130
Gendermale 2.447 0.4087 1.0913 5.487
Concordance= 0.71 (se = 0.036 )
Likelihood ratio test= 26.84 on 3 df, p=6e-06
Wald test = 23.16 on 3 df, p=4e-05
Score (logrank) test = 23.81 on 3 df, p=3e-05
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: -0.110904
Standard error............: 0.298774
Odds ratio (effect size)..: 0.895
Lower 95% CI..............: 0.498
Upper 95% CI..............: 1.608
T-value...................: -0.371199
P-value...................: 0.7104893
Sample size in model......: 1184
Number of events..........: 45
> processing [MCP1_rank]; 2 out of 2 proteins.
> cross tabulation of MCP1_rank-stratum.
[-2.41053,0.00444) [ 0.00444,3.12635]
283 282
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender, data = TEMP.DF)
n= 558, number of events= 27
(1863 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] -0.05233 0.94902 0.38729 -0.135 0.8925
Age 0.05687 1.05852 0.02468 2.304 0.0212 *
Gendermale 0.79435 2.21300 0.54234 1.465 0.1430
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 0.949 1.0537 0.4442 2.027
Age 1.059 0.9447 1.0085 1.111
Gendermale 2.213 0.4519 0.7644 6.407
Concordance= 0.667 (se = 0.059 )
Likelihood ratio test= 8.48 on 3 df, p=0.04
Wald test = 7.69 on 3 df, p=0.05
Score (logrank) test = 7.85 on 3 df, p=0.05
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: MCP1_rank
Effect size...............: -0.052326
Standard error............: 0.387295
Odds ratio (effect size)..: 0.949
Lower 95% CI..............: 0.444
Upper 95% CI..............: 2.027
T-value...................: -0.135107
P-value...................: 0.8925272
Sample size in model......: 558
Number of events..........: 27 
cat("- Edit the column names...\n")- Edit the column names...colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")- Correct the variable types...COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
cat("- Writing results to Excel-file...\n")- Writing results to Excel-file...head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")- Removing intermediate files...#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
#rm(head.style)
MODEL 2
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
eptime = times[i]
ep = endpoints[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
#as.integer(TEMP.DF[,ep] == "Excluded")
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
# ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [years]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL2.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}* Analyzing the effect of plaque proteins on [epmajor.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ug_2015_rank]; 1 out of 2 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34055,0.00105) [ 0.00105,3.34055]
598 598
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 1027, number of events= 115
(1394 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 1.327e-01 1.142e+00 1.898e-01 0.699 0.4843
Age 3.278e-02 1.033e+00 1.275e-02 2.572 0.0101 *
Gendermale 3.790e-01 1.461e+00 2.280e-01 1.662 0.0964 .
Hypertension.compositeno -4.193e-01 6.575e-01 3.564e-01 -1.177 0.2393
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -1.633e-02 9.838e-01 2.234e-01 -0.073 0.9417
SmokerStatusEx-smoker -5.028e-01 6.049e-01 2.095e-01 -2.400 0.0164 *
SmokerStatusNever smoked -8.142e-01 4.430e-01 3.415e-01 -2.385 0.0171 *
Med.Statin.LLDno 2.579e-01 1.294e+00 2.155e-01 1.197 0.2314
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 4.283e-01 1.535e+00 2.635e-01 1.626 0.1040
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.969e-02 9.805e-01 4.925e-03 -3.997 6.42e-05 ***
BMI 5.402e-02 1.056e+00 2.613e-02 2.067 0.0387 *
MedHx_CVDyes 5.306e-01 1.700e+00 2.220e-01 2.391 0.0168 *
stenose0-49% -1.560e+01 1.677e-07 2.462e+03 -0.006 0.9949
stenose50-70% -8.183e-01 4.412e-01 8.701e-01 -0.941 0.3470
stenose70-90% -2.441e-01 7.834e-01 7.298e-01 -0.335 0.7380
stenose90-99% -2.079e-01 8.123e-01 7.282e-01 -0.285 0.7753
stenose100% (Occlusion) -4.754e-02 9.536e-01 1.244e+00 -0.038 0.9695
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.529e+01 2.289e-07 2.920e+03 -0.005 0.9958
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 1.142e+00 8.757e-01 0.78722 1.6565
Age 1.033e+00 9.678e-01 1.00783 1.0595
Gendermale 1.461e+00 6.845e-01 0.93440 2.2840
Hypertension.compositeno 6.575e-01 1.521e+00 0.32699 1.3220
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.838e-01 1.016e+00 0.63498 1.5242
SmokerStatusEx-smoker 6.049e-01 1.653e+00 0.40120 0.9119
SmokerStatusNever smoked 4.430e-01 2.257e+00 0.22685 0.8650
Med.Statin.LLDno 1.294e+00 7.727e-01 0.84830 1.9746
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.535e+00 6.516e-01 0.91566 2.5720
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.805e-01 1.020e+00 0.97109 0.9900
BMI 1.056e+00 9.474e-01 1.00281 1.1110
MedHx_CVDyes 1.700e+00 5.882e-01 1.10031 2.6266
stenose0-49% 1.677e-07 5.962e+06 0.00000 Inf
stenose50-70% 4.412e-01 2.267e+00 0.08017 2.4278
stenose70-90% 7.834e-01 1.276e+00 0.18741 3.2747
stenose90-99% 8.123e-01 1.231e+00 0.19494 3.3848
stenose100% (Occlusion) 9.536e-01 1.049e+00 0.08333 10.9119
stenoseNA NA NA NA NA
stenose50-99% 2.289e-07 4.369e+06 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.697 (se = 0.023 )
Likelihood ratio test= 63.79 on 18 df, p=5e-07
Wald test = 58.83 on 18 df, p=3e-06
Score (logrank) test = 62.19 on 18 df, p=9e-07
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: 0.132746
Standard error............: 0.189795
Odds ratio (effect size)..: 1.142
Lower 95% CI..............: 0.787
Upper 95% CI..............: 1.657
T-value...................: 0.699416
P-value...................: 0.4842918
Sample size in model......: 1027
Number of events..........: 115
> processing [MCP1_rank]; 2 out of 2 proteins.
> cross tabulation of MCP1_rank-stratum.
[-2.41053,0.00444) [ 0.00444,3.12635]
283 282
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 500, number of events= 63
(1921 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] -3.675e-01 6.925e-01 2.589e-01 -1.419 0.1558
Age 3.584e-02 1.036e+00 1.783e-02 2.010 0.0444 *
Gendermale 7.248e-01 2.064e+00 3.488e-01 2.078 0.0377 *
Hypertension.compositeno -7.758e-01 4.603e-01 5.297e-01 -1.465 0.1430
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 6.512e-01 1.918e+00 2.888e-01 2.255 0.0241 *
SmokerStatusEx-smoker -6.494e-01 5.224e-01 2.820e-01 -2.303 0.0213 *
SmokerStatusNever smoked -4.079e-01 6.651e-01 4.276e-01 -0.954 0.3402
Med.Statin.LLDno 2.625e-01 1.300e+00 2.872e-01 0.914 0.3608
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.379e-01 1.148e+00 4.211e-01 0.327 0.7433
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.062e-02 9.894e-01 6.659e-03 -1.595 0.1108
BMI 1.197e-02 1.012e+00 3.338e-02 0.359 0.7199
MedHx_CVDyes 6.103e-01 1.841e+00 3.058e-01 1.996 0.0460 *
stenose0-49% -1.648e+01 6.978e-08 3.445e+03 -0.005 0.9962
stenose50-70% -1.621e+00 1.977e-01 1.446e+00 -1.121 0.2622
stenose70-90% -7.117e-01 4.908e-01 1.047e+00 -0.680 0.4967
stenose90-99% -9.422e-01 3.898e-01 1.052e+00 -0.895 0.3706
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 6.925e-01 1.444e+00 0.41691 1.1502
Age 1.036e+00 9.648e-01 1.00090 1.0733
Gendermale 2.064e+00 4.844e-01 1.04204 4.0898
Hypertension.compositeno 4.603e-01 2.172e+00 0.16298 1.3001
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.918e+00 5.214e-01 1.08899 3.3776
SmokerStatusEx-smoker 5.224e-01 1.914e+00 0.30055 0.9079
SmokerStatusNever smoked 6.651e-01 1.504e+00 0.28765 1.5377
Med.Statin.LLDno 1.300e+00 7.691e-01 0.74050 2.2827
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.148e+00 8.712e-01 0.50285 2.6202
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.894e-01 1.011e+00 0.97661 1.0024
BMI 1.012e+00 9.881e-01 0.94796 1.0805
MedHx_CVDyes 1.841e+00 5.432e-01 1.01103 3.3524
stenose0-49% 6.978e-08 1.433e+07 0.00000 Inf
stenose50-70% 1.977e-01 5.059e+00 0.01161 3.3636
stenose70-90% 4.908e-01 2.038e+00 0.06304 3.8212
stenose90-99% 3.898e-01 2.566e+00 0.04955 3.0660
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.71 (se = 0.029 )
Likelihood ratio test= 36 on 16 df, p=0.003
Wald test = 32.13 on 16 df, p=0.01
Score (logrank) test = 34.35 on 16 df, p=0.005
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: MCP1_rank
Effect size...............: -0.367486
Standard error............: 0.258888
Odds ratio (effect size)..: 0.692
Lower 95% CI..............: 0.417
Upper 95% CI..............: 1.15
T-value...................: -1.419482
P-value...................: 0.1557587
Sample size in model......: 500
Number of events..........: 63
* Analyzing the effect of plaque proteins on [epstroke.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ug_2015_rank]; 1 out of 2 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34055,0.00105) [ 0.00105,3.34055]
598 598
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 1027, number of events= 59
(1394 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 1.934e-01 1.213e+00 2.668e-01 0.725 0.4684
Age 4.267e-02 1.044e+00 1.756e-02 2.429 0.0151 *
Gendermale -4.383e-02 9.571e-01 2.992e-01 -0.146 0.8835
Hypertension.compositeno 8.567e-03 1.009e+00 4.178e-01 0.021 0.9836
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -2.315e-02 9.771e-01 3.168e-01 -0.073 0.9418
SmokerStatusEx-smoker -1.168e-01 8.898e-01 2.964e-01 -0.394 0.6936
SmokerStatusNever smoked -9.611e-01 3.825e-01 5.239e-01 -1.835 0.0666 .
Med.Statin.LLDno 3.743e-01 1.454e+00 2.926e-01 1.279 0.2008
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 3.817e-01 1.465e+00 3.714e-01 1.028 0.3040
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -4.331e-03 9.957e-01 6.992e-03 -0.619 0.5357
BMI 8.171e-02 1.085e+00 3.459e-02 2.362 0.0182 *
MedHx_CVDyes 3.652e-01 1.441e+00 2.940e-01 1.242 0.2142
stenose0-49% -1.523e+01 2.419e-07 3.388e+03 -0.004 0.9964
stenose50-70% -5.088e-01 6.012e-01 1.160e+00 -0.438 0.6610
stenose70-90% -2.645e-01 7.676e-01 1.029e+00 -0.257 0.7971
stenose90-99% -2.597e-01 7.713e-01 1.029e+00 -0.252 0.8008
stenose100% (Occlusion) 5.974e-01 1.817e+00 1.439e+00 0.415 0.6780
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.515e+01 2.629e-07 3.962e+03 -0.004 0.9969
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 1.213e+00 8.242e-01 0.71934 2.047
Age 1.044e+00 9.582e-01 1.00828 1.080
Gendermale 9.571e-01 1.045e+00 0.53242 1.721
Hypertension.compositeno 1.009e+00 9.915e-01 0.44469 2.288
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.771e-01 1.023e+00 0.52513 1.818
SmokerStatusEx-smoker 8.898e-01 1.124e+00 0.49772 1.591
SmokerStatusNever smoked 3.825e-01 2.615e+00 0.13699 1.068
Med.Statin.LLDno 1.454e+00 6.878e-01 0.81941 2.580
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.465e+00 6.827e-01 0.70740 3.033
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.957e-01 1.004e+00 0.98213 1.009
BMI 1.085e+00 9.215e-01 1.01402 1.161
MedHx_CVDyes 1.441e+00 6.940e-01 0.80970 2.564
stenose0-49% 2.419e-07 4.133e+06 0.00000 Inf
stenose50-70% 6.012e-01 1.663e+00 0.06185 5.844
stenose70-90% 7.676e-01 1.303e+00 0.10217 5.767
stenose90-99% 7.713e-01 1.297e+00 0.10256 5.800
stenose100% (Occlusion) 1.817e+00 5.503e-01 0.10830 30.495
stenoseNA NA NA NA NA
stenose50-99% 2.629e-07 3.804e+06 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.67 (se = 0.034 )
Likelihood ratio test= 23.02 on 18 df, p=0.2
Wald test = 20.97 on 18 df, p=0.3
Score (logrank) test = 22.15 on 18 df, p=0.2
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: 0.1934
Standard error............: 0.26675
Odds ratio (effect size)..: 1.213
Lower 95% CI..............: 0.719
Upper 95% CI..............: 2.047
T-value...................: 0.725021
P-value...................: 0.4684393
Sample size in model......: 1027
Number of events..........: 59
> processing [MCP1_rank]; 2 out of 2 proteins.
> cross tabulation of MCP1_rank-stratum.
[-2.41053,0.00444) [ 0.00444,3.12635]
283 282
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 500, number of events= 31
(1921 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] -4.542e-01 6.349e-01 3.745e-01 -1.213 0.225
Age 2.599e-02 1.026e+00 2.425e-02 1.072 0.284
Gendermale -8.988e-03 9.911e-01 4.157e-01 -0.022 0.983
Hypertension.compositeno -8.325e-01 4.349e-01 7.489e-01 -1.112 0.266
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 2.859e-01 1.331e+00 4.339e-01 0.659 0.510
SmokerStatusEx-smoker -5.580e-01 5.724e-01 4.020e-01 -1.388 0.165
SmokerStatusNever smoked -5.078e-01 6.018e-01 6.118e-01 -0.830 0.407
Med.Statin.LLDno 1.409e-02 1.014e+00 4.296e-01 0.033 0.974
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 2.310e-01 1.260e+00 5.821e-01 0.397 0.691
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD 1.638e-03 1.002e+00 9.872e-03 0.166 0.868
BMI 1.181e-02 1.012e+00 4.625e-02 0.255 0.798
MedHx_CVDyes 3.573e-01 1.430e+00 4.115e-01 0.868 0.385
stenose0-49% -1.863e+01 8.109e-09 1.293e+04 -0.001 0.999
stenose50-70% -1.832e+01 1.106e-08 4.838e+03 -0.004 0.997
stenose70-90% -1.231e+00 2.921e-01 1.108e+00 -1.111 0.267
stenose90-99% -1.333e+00 2.637e-01 1.119e+00 -1.192 0.233
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 6.349e-01 1.575e+00 0.30474 1.323
Age 1.026e+00 9.743e-01 0.97868 1.076
Gendermale 9.911e-01 1.009e+00 0.43880 2.238
Hypertension.compositeno 4.349e-01 2.299e+00 0.10022 1.888
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.331e+00 7.513e-01 0.56864 3.116
SmokerStatusEx-smoker 5.724e-01 1.747e+00 0.26029 1.259
SmokerStatusNever smoked 6.018e-01 1.662e+00 0.18143 1.996
Med.Statin.LLDno 1.014e+00 9.860e-01 0.43693 2.354
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.260e+00 7.937e-01 0.40258 3.943
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 1.002e+00 9.984e-01 0.98245 1.021
BMI 1.012e+00 9.883e-01 0.92419 1.108
MedHx_CVDyes 1.430e+00 6.995e-01 0.63817 3.202
stenose0-49% 8.109e-09 1.233e+08 0.00000 Inf
stenose50-70% 1.106e-08 9.043e+07 0.00000 Inf
stenose70-90% 2.921e-01 3.424e+00 0.03329 2.563
stenose90-99% 2.637e-01 3.792e+00 0.02945 2.362
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.666 (se = 0.047 )
Likelihood ratio test= 11.69 on 16 df, p=0.8
Wald test = 8.84 on 16 df, p=0.9
Score (logrank) test = 10.84 on 16 df, p=0.8
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: MCP1_rank
Effect size...............: -0.454221
Standard error............: 0.374539
Odds ratio (effect size)..: 0.635
Lower 95% CI..............: 0.305
Upper 95% CI..............: 1.323
T-value...................: -1.212747
P-value...................: 0.2252264
Sample size in model......: 500
Number of events..........: 31
* Analyzing the effect of plaque proteins on [epcoronary.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ug_2015_rank]; 1 out of 2 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34055,0.00105) [ 0.00105,3.34055]
598 598
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 1027, number of events= 78
(1394 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] -1.290e-01 8.790e-01 2.315e-01 -0.557 0.577222
Age -8.243e-04 9.992e-01 1.506e-02 -0.055 0.956352
Gendermale 8.625e-01 2.369e+00 3.029e-01 2.848 0.004404 **
Hypertension.compositeno -9.192e-01 3.989e-01 5.209e-01 -1.765 0.077619 .
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -1.045e-01 9.008e-01 2.759e-01 -0.379 0.704879
SmokerStatusEx-smoker -6.291e-01 5.331e-01 2.573e-01 -2.445 0.014496 *
SmokerStatusNever smoked -2.735e-01 7.607e-01 3.647e-01 -0.750 0.453241
Med.Statin.LLDno 1.147e-01 1.122e+00 2.730e-01 0.420 0.674465
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 3.664e-01 1.443e+00 3.348e-01 1.095 0.273710
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -2.059e-02 9.796e-01 5.940e-03 -3.466 0.000528 ***
BMI 1.350e-02 1.014e+00 3.290e-02 0.410 0.681500
MedHx_CVDyes 6.867e-01 1.987e+00 2.795e-01 2.457 0.014000 *
stenose0-49% -1.584e+01 1.320e-07 3.048e+03 -0.005 0.995853
stenose50-70% -1.713e+00 1.803e-01 1.418e+00 -1.208 0.227098
stenose70-90% -1.372e-01 8.718e-01 1.022e+00 -0.134 0.893141
stenose90-99% -1.918e-01 8.255e-01 1.022e+00 -0.188 0.851135
stenose100% (Occlusion) -1.540e+01 2.059e-07 2.452e+03 -0.006 0.994990
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% 8.142e-01 2.257e+00 1.429e+00 0.570 0.568977
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 8.790e-01 1.138e+00 0.55840 1.3835
Age 9.992e-01 1.001e+00 0.97011 1.0291
Gendermale 2.369e+00 4.221e-01 1.30849 4.2894
Hypertension.compositeno 3.989e-01 2.507e+00 0.14370 1.1071
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.008e-01 1.110e+00 0.52459 1.5468
SmokerStatusEx-smoker 5.331e-01 1.876e+00 0.32192 0.8827
SmokerStatusNever smoked 7.607e-01 1.315e+00 0.37224 1.5546
Med.Statin.LLDno 1.122e+00 8.917e-01 0.65677 1.9151
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.443e+00 6.932e-01 0.74849 2.7802
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.796e-01 1.021e+00 0.96828 0.9911
BMI 1.014e+00 9.866e-01 0.95030 1.0811
MedHx_CVDyes 1.987e+00 5.032e-01 1.14909 3.4365
stenose0-49% 1.320e-07 7.576e+06 0.00000 Inf
stenose50-70% 1.803e-01 5.547e+00 0.01118 2.9061
stenose70-90% 8.718e-01 1.147e+00 0.11772 6.4559
stenose90-99% 8.255e-01 1.211e+00 0.11137 6.1183
stenose100% (Occlusion) 2.059e-07 4.857e+06 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% 2.257e+00 4.430e-01 0.13703 37.1835
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.726 (se = 0.028 )
Likelihood ratio test= 49.9 on 18 df, p=8e-05
Wald test = 43.46 on 18 df, p=7e-04
Score (logrank) test = 47.19 on 18 df, p=2e-04
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: -0.129025
Standard error............: 0.231458
Odds ratio (effect size)..: 0.879
Lower 95% CI..............: 0.558
Upper 95% CI..............: 1.384
T-value...................: -0.557447
P-value...................: 0.577222
Sample size in model......: 1027
Number of events..........: 78
> processing [MCP1_rank]; 2 out of 2 proteins.
> cross tabulation of MCP1_rank-stratum.
[-2.41053,0.00444) [ 0.00444,3.12635]
283 282
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 500, number of events= 43
(1921 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 1.253e-01 1.133e+00 3.124e-01 0.401 0.6884
Age 4.090e-02 1.042e+00 2.220e-02 1.842 0.0655 .
Gendermale 7.821e-01 2.186e+00 4.313e-01 1.813 0.0698 .
Hypertension.compositeno -2.259e-01 7.978e-01 5.429e-01 -0.416 0.6773
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 6.105e-01 1.841e+00 3.477e-01 1.756 0.0791 .
SmokerStatusEx-smoker -5.187e-01 5.953e-01 3.433e-01 -1.511 0.1308
SmokerStatusNever smoked -2.387e-01 7.877e-01 4.981e-01 -0.479 0.6318
Med.Statin.LLDno -2.555e-03 9.974e-01 3.595e-01 -0.007 0.9943
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 4.653e-01 1.592e+00 4.522e-01 1.029 0.3035
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.450e-02 9.856e-01 8.414e-03 -1.724 0.0847 .
BMI 3.083e-02 1.031e+00 4.038e-02 0.763 0.4452
MedHx_CVDyes 2.200e-01 1.246e+00 3.438e-01 0.640 0.5223
stenose0-49% -4.999e-01 6.066e-01 8.663e+03 0.000 1.0000
stenose50-70% 1.591e+01 8.106e+06 4.981e+03 0.003 0.9975
stenose70-90% 1.619e+01 1.075e+07 4.981e+03 0.003 0.9974
stenose90-99% 1.612e+01 1.007e+07 4.981e+03 0.003 0.9974
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 1.133e+00 8.823e-01 0.6145 2.091
Age 1.042e+00 9.599e-01 0.9974 1.088
Gendermale 2.186e+00 4.574e-01 0.9387 5.091
Hypertension.compositeno 7.978e-01 1.253e+00 0.2753 2.312
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.841e+00 5.431e-01 0.9315 3.640
SmokerStatusEx-smoker 5.953e-01 1.680e+00 0.3038 1.167
SmokerStatusNever smoked 7.877e-01 1.270e+00 0.2967 2.091
Med.Statin.LLDno 9.974e-01 1.003e+00 0.4931 2.018
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.592e+00 6.279e-01 0.6563 3.864
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.856e-01 1.015e+00 0.9695 1.002
BMI 1.031e+00 9.696e-01 0.9528 1.116
MedHx_CVDyes 1.246e+00 8.025e-01 0.6352 2.444
stenose0-49% 6.066e-01 1.649e+00 0.0000 Inf
stenose50-70% 8.106e+06 1.234e-07 0.0000 Inf
stenose70-90% 1.075e+07 9.306e-08 0.0000 Inf
stenose90-99% 1.007e+07 9.933e-08 0.0000 Inf
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.7 (se = 0.038 )
Likelihood ratio test= 21.95 on 16 df, p=0.1
Wald test = 13.13 on 16 df, p=0.7
Score (logrank) test = 21.53 on 16 df, p=0.2
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: MCP1_rank
Effect size...............: 0.125261
Standard error............: 0.312363
Odds ratio (effect size)..: 1.133
Lower 95% CI..............: 0.614
Upper 95% CI..............: 2.091
T-value...................: 0.401012
P-value...................: 0.6884117
Sample size in model......: 500
Number of events..........: 43
* Analyzing the effect of plaque proteins on [epcvdeath.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ug_2015_rank]; 1 out of 2 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34055,0.00105) [ 0.00105,3.34055]
598 598
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 1027, number of events= 33
(1394 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] -2.044e-01 8.152e-01 3.583e-01 -0.570 0.56836
Age 6.640e-02 1.069e+00 2.655e-02 2.501 0.01238 *
Gendermale 1.258e+00 3.520e+00 5.565e-01 2.261 0.02373 *
Hypertension.compositeno -1.774e+01 1.978e-08 4.017e+03 -0.004 0.99648
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -3.835e-02 9.624e-01 4.261e-01 -0.090 0.92829
SmokerStatusEx-smoker -5.672e-01 5.671e-01 4.021e-01 -1.411 0.15834
SmokerStatusNever smoked -4.302e-01 6.504e-01 6.158e-01 -0.699 0.48479
Med.Statin.LLDno 8.681e-02 1.091e+00 4.156e-01 0.209 0.83454
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.130e+00 3.096e+00 4.161e-01 2.716 0.00661 **
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -3.395e-02 9.666e-01 9.369e-03 -3.624 0.00029 ***
BMI 8.410e-02 1.088e+00 5.118e-02 1.643 0.10031
MedHx_CVDyes 7.683e-01 2.156e+00 4.605e-01 1.668 0.09528 .
stenose0-49% -2.031e+01 1.509e-09 2.713e+04 -0.001 0.99940
stenose50-70% -1.041e+00 3.531e-01 1.236e+00 -0.842 0.39970
stenose70-90% -1.480e+00 2.276e-01 1.069e+00 -1.385 0.16609
stenose90-99% -1.069e+00 3.434e-01 1.054e+00 -1.014 0.31055
stenose100% (Occlusion) -1.957e+01 3.154e-09 1.974e+04 -0.001 0.99921
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.939e+01 3.802e-09 3.407e+04 -0.001 0.99955
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 8.152e-01 1.227e+00 0.40392 1.6451
Age 1.069e+00 9.358e-01 1.01447 1.1257
Gendermale 3.520e+00 2.841e-01 1.18264 10.4751
Hypertension.compositeno 1.978e-08 5.054e+07 0.00000 Inf
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.624e-01 1.039e+00 0.41748 2.2185
SmokerStatusEx-smoker 5.671e-01 1.763e+00 0.25789 1.2471
SmokerStatusNever smoked 6.504e-01 1.538e+00 0.19453 2.1744
Med.Statin.LLDno 1.091e+00 9.168e-01 0.48298 2.4631
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 3.096e+00 3.230e-01 1.36967 6.9988
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.666e-01 1.035e+00 0.94903 0.9845
BMI 1.088e+00 9.193e-01 0.98393 1.2025
MedHx_CVDyes 2.156e+00 4.638e-01 0.87427 5.3170
stenose0-49% 1.509e-09 6.625e+08 0.00000 Inf
stenose50-70% 3.531e-01 2.832e+00 0.03132 3.9813
stenose70-90% 2.276e-01 4.394e+00 0.02802 1.8490
stenose90-99% 3.434e-01 2.912e+00 0.04351 2.7102
stenose100% (Occlusion) 3.154e-09 3.170e+08 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% 3.802e-09 2.630e+08 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.839 (se = 0.031 )
Likelihood ratio test= 60.15 on 18 df, p=2e-06
Wald test = 20.83 on 18 df, p=0.3
Score (logrank) test = 56.03 on 18 df, p=9e-06
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: -0.204374
Standard error............: 0.358254
Odds ratio (effect size)..: 0.815
Lower 95% CI..............: 0.404
Upper 95% CI..............: 1.645
T-value...................: -0.570471
P-value...................: 0.568358
Sample size in model......: 1027
Number of events..........: 33
> processing [MCP1_rank]; 2 out of 2 proteins.
> cross tabulation of MCP1_rank-stratum.
[-2.41053,0.00444) [ 0.00444,3.12635]
283 282
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 500, number of events= 24
(1921 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] -2.576e-01 7.729e-01 4.252e-01 -0.606 0.5446
Age 5.316e-02 1.055e+00 3.097e-02 1.716 0.0861 .
Gendermale 7.385e-01 2.093e+00 5.903e-01 1.251 0.2110
Hypertension.compositeno -1.803e+01 1.483e-08 4.352e+03 -0.004 0.9967
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 7.152e-01 2.045e+00 5.028e-01 1.423 0.1549
SmokerStatusEx-smoker -7.598e-01 4.678e-01 4.599e-01 -1.652 0.0985 .
SmokerStatusNever smoked -3.896e-01 6.773e-01 7.130e-01 -0.546 0.5847
Med.Statin.LLDno 7.126e-01 2.039e+00 4.465e-01 1.596 0.1105
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 3.557e-01 1.427e+00 6.483e-01 0.549 0.5832
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.915e-02 9.810e-01 1.013e-02 -1.890 0.0587 .
BMI 6.416e-03 1.006e+00 5.548e-02 0.116 0.9079
MedHx_CVDyes 1.350e+00 3.859e+00 6.334e-01 2.132 0.0330 *
stenose0-49% -8.695e-01 4.192e-01 3.641e+04 0.000 1.0000
stenose50-70% 3.413e-01 1.407e+00 2.166e+04 0.000 1.0000
stenose70-90% 1.825e+01 8.405e+07 2.003e+04 0.001 0.9993
stenose90-99% 1.800e+01 6.576e+07 2.003e+04 0.001 0.9993
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 7.729e-01 1.294e+00 0.3359 1.779
Age 1.055e+00 9.482e-01 0.9925 1.121
Gendermale 2.093e+00 4.778e-01 0.6580 6.656
Hypertension.compositeno 1.483e-08 6.741e+07 0.0000 Inf
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 2.045e+00 4.891e-01 0.7632 5.478
SmokerStatusEx-smoker 4.678e-01 2.138e+00 0.1899 1.152
SmokerStatusNever smoked 6.773e-01 1.476e+00 0.1675 2.739
Med.Statin.LLDno 2.039e+00 4.904e-01 0.8500 4.892
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.427e+00 7.007e-01 0.4005 5.086
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.810e-01 1.019e+00 0.9617 1.001
BMI 1.006e+00 9.936e-01 0.9027 1.122
MedHx_CVDyes 3.859e+00 2.591e-01 1.1152 13.355
stenose0-49% 4.192e-01 2.386e+00 0.0000 Inf
stenose50-70% 1.407e+00 7.108e-01 0.0000 Inf
stenose70-90% 8.405e+07 1.190e-08 0.0000 Inf
stenose90-99% 6.576e+07 1.521e-08 0.0000 Inf
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.812 (se = 0.037 )
Likelihood ratio test= 33.67 on 16 df, p=0.006
Wald test = 12.25 on 16 df, p=0.7
Score (logrank) test = 28.24 on 16 df, p=0.03
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: MCP1_rank
Effect size...............: -0.257626
Standard error............: 0.425216
Odds ratio (effect size)..: 0.773
Lower 95% CI..............: 0.336
Upper 95% CI..............: 1.779
T-value...................: -0.60587
P-value...................: 0.5446008
Sample size in model......: 500
Number of events..........: 24 
cat("- Edit the column names...\n")- Edit the column names...colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")- Correct the variable types...COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
cat("- Writing results to Excel-file...\n")- Writing results to Excel-file...head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")- Removing intermediate files...rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
rm(head.style)object 'head.style' not found30-days follow-up
MODEL 1
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times30)){
eptime = times30[i]
ep = endpoints30[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
ylim = c(0.75, 1),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".30days.pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 1 (Simple model)
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
# ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [days]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL1.30days.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}
cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.30days.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
#rm(head.style)
MODEL 2
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times30)){
eptime = times30[i]
ep = endpoints30[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
#as.integer(TEMP.DF[,ep] == "Excluded")
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
ylim = c(0.75, 1),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".30days.pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
# ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [days]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL2.30days.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}
cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.30days.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
rm(head.style)
90-days follow-up
MODEL 1
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times90)){
eptime = times90[i]
ep = endpoints90[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
ylim = c(0.75, 1),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".90days.pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 1 (Simple model)
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
# ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [days]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL1.90days.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}
cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.90days.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
#rm(head.style)
MODEL 2
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times90)){
eptime = times90[i]
ep = endpoints90[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
#as.integer(TEMP.DF[,ep] == "Excluded")
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
ylim = c(0.75, 1),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".90days.pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
# ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [days]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL2.90days.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}
cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.90days.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
rm(head.style)
Correlations
All biomarkers
We correlated serum and plaque levels of the biomarkers.
# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools))
install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")Skipping install of 'ggcorrplot' from a github remote, the SHA1 (c46b4cce) has not changed since last install.
Use `force = TRUE` to force installationlibrary(ggcorrplot)
# Creating matrix - inverse-rank transformation
# --------------------------------
# AEDB.CEA.temp <- subset(AEDB.CEA,
# select = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank",
# TRAITS.BIN, TRAITS.CON.RANK)
# )
AEDB.CEA.temp <- subset(AEDB.CEA,
select = c("MCP1_rank", "MCP1_pg_ug_2015_rank",
TRAITS.BIN, TRAITS.CON.RANK)
)
AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
str(AEDB.CEA.temp)tibble [2,421 × 9] (S3: tbl_df/tbl/data.frame)
$ MCP1_rank : num [1:2421] 0.759 -0.944 1.949 0.736 0.69 ...
$ MCP1_pg_ug_2015_rank: num [1:2421] NA 0.937 2.158 1.209 1.996 ...
$ CalcificationPlaque : num [1:2421] 1 1 1 1 1 2 2 1 2 1 ...
$ CollagenPlaque : num [1:2421] 1 2 2 2 2 1 NA 2 2 2 ...
$ Fat10Perc : num [1:2421] 2 2 2 2 2 2 2 2 2 2 ...
$ IPH : num [1:2421] 2 2 2 2 1 2 2 2 2 2 ...
$ Macrophages_rank : num [1:2421] 1.388 1.12 1.365 0.721 0.396 ...
$ SMC_rank : num [1:2421] 1.67023 1.13089 0.00295 1.42623 1.2689 ...
$ VesselDensity_rank : num [1:2421] -0.529 -0.977 -0.774 0.716 1.099 ...AEDB.CEA.matrix.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)
corr_biomarkers.rank <- round(cor(AEDB.CEA.matrix.RANK,
use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
method = "spearman"), 3)
# corr_biomarkers.rank
corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.RANK, use = "pairwise.complete.obs", method = "spearman")Cannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with ties# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers.rank,
method = "square",
type = "lower",
title = "Cross biomarker correlations",
show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
show.diag = TRUE,
hc.order = FALSE,
lab = FALSE,
digits = 3,
# p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
colors = c("#1290D9", "#FFFFFF", "#E55738"))
# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
ut <- upper.tri(cormat)
data.frame(
biomarker_row = rownames(cormat)[row(cormat)[ut]],
biomarker_column = rownames(cormat)[col(cormat)[ut]],
spearman_cor =(cormat)[ut],
pval = pmat[ut]
)
}
corr_biomarkers.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank))
DT::datatable(corr_biomarkers.rank.df)
# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall",
"spearman"), ...)
{
x = checkData(R, method = "matrix")
if (missing(method))
method = method[1]
cormeth <- method
panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs",
method = cormeth, cex.cor, ...) {
usr <- par("usr")
on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
r <- cor(x, y, use = use, method = method)
txt <- format(c(r, 0.123456789), digits = digits)[1]
txt <- paste(prefix, txt, sep = "")
if (missing(cex.cor))
cex <- 0.8/strwidth(txt)
test <- cor.test(as.numeric(x), as.numeric(y), method = method)
Signif <- symnum(test$p.value, corr = FALSE, na = FALSE,
cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***",
"**", "*", ".", " "))
text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
text(0.8, 0.8, Signif, cex = cex, col = 2)
}
f <- function(t) {
dnorm(t, mean = mean(x), sd = sd.xts(x))
}
dotargs <- list(...)
dotargs$method <- NULL
rm(method)
hist.panel = function(x, ... = NULL) {
par(new = TRUE)
hist(x, col = "#1290D9", probability = TRUE, axes = FALSE,
# hist(x, col = "light gray", probability = TRUE, axes = FALSE,
main = "", breaks = "FD")
lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
rug(x)
}
if (histogram)
pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor,
diag.panel = hist.panel, ...)
else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}
chart.Correlation.new(AEDB.CEA.matrix.RANK, method = "spearman", histogram = TRUE, pch = 3)
# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")
# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables
library(GGally)
# From the help page:
ggpairs(AEDB.CEA,
columns = c("MCP1_rank", "MCP1_pg_ug_2015_rank", TRAITS.BIN, TRAITS.CON.RANK),
columnLabels = c("MCP1 (serum)", "MCP1",
"Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density"),
method = c("spearman"),
# ggplot2::aes(colour = Gender),
progress = FALSE) Extra arguments: 'method' are being ignored. If these are meant to be aesthetics, submit them using the 'mapping' variable within ggpairs with ggplot2::aes or ggplot2::aes_string.
Circulating MCP1
Finally, we explored in a sub-sample, where circulating MCP-1 levels are available, the following:
- A correlation between MCP-1 levels in the plaque and circulating MCP-1 levels
- Associations of circulating MCP-1 levels with plaque vulnerability characteristics
- Associations of circulating MCP-1 levels with the status of the plaque in terms of presence of symptoms (symptomatic vs. asymptomatic)
- Associations of circulating MCP-1 levels with the primary composite endpoint of secondary cardiovascular events.
# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools))
install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")Skipping install of 'ggcorrplot' from a github remote, the SHA1 (c46b4cce) has not changed since last install.
Use `force = TRUE` to force installationlibrary(ggcorrplot)
# Creating matrix - inverse-rank transformation
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA,
select = c("MCP1_rank",
TRAITS.BIN, TRAITS.CON.RANK,
"Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite")
)
AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
AEDB.CEA.temp$Symptoms.5G <- as.numeric(AEDB.CEA.temp$Symptoms.5G)
AEDB.CEA.temp$AsymptSympt <- as.numeric(AEDB.CEA.temp$AsymptSympt)
AEDB.CEA.temp$EP_major <- as.numeric(AEDB.CEA.temp$EP_major)
AEDB.CEA.temp$EP_composite <- as.numeric(AEDB.CEA.temp$EP_composite)
# str(AEDB.CEA.temp)
AEDB.CEA.matrix.serum.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)
corr_biomarkers_serum.rank <- round(cor(AEDB.CEA.matrix.serum.RANK,
use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
method = "spearman"), 3)
# corr_biomarkers.rank
corr_biomarkers_serum_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.serum.RANK, use = "pairwise.complete.obs", method = "spearman")Cannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with ties# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers_serum.rank,
method = "square",
type = "lower",
title = "Cross biomarker correlations",
show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
show.diag = TRUE,
hc.order = FALSE,
lab = FALSE,
digits = 3,
# p.mat = corr_biomarkers_serum_p.rank, sig.level = 0.05,
colors = c("#1290D9", "#FFFFFF", "#E55738"))
# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
ut <- upper.tri(cormat)
data.frame(
biomarker_row = rownames(cormat)[row(cormat)[ut]],
biomarker_column = rownames(cormat)[col(cormat)[ut]],
spearman_cor =(cormat)[ut],
pval = pmat[ut]
)
}
corr_biomarkers_serum.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers_serum.rank, corr_biomarkers_serum_p.rank))
DT::datatable(corr_biomarkers_serum.rank.df)
# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall",
"spearman"), ...)
{
x = checkData(R, method = "matrix")
if (missing(method))
method = method[1]
cormeth <- method
panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs",
method = cormeth, cex.cor, ...) {
usr <- par("usr")
on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
r <- cor(x, y, use = use, method = method)
txt <- format(c(r, 0.123456789), digits = digits)[1]
txt <- paste(prefix, txt, sep = "")
if (missing(cex.cor))
cex <- 0.8/strwidth(txt)
test <- cor.test(as.numeric(x), as.numeric(y), method = method)
Signif <- symnum(test$p.value, corr = FALSE, na = FALSE,
cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***",
"**", "*", ".", " "))
text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
text(0.8, 0.8, Signif, cex = cex, col = 2)
}
f <- function(t) {
dnorm(t, mean = mean(x), sd = sd.xts(x))
}
dotargs <- list(...)
dotargs$method <- NULL
rm(method)
hist.panel = function(x, ... = NULL) {
par(new = TRUE)
hist(x, col = "#1290D9", probability = TRUE, axes = FALSE,
# hist(x, col = "light gray", probability = TRUE, axes = FALSE,
main = "", breaks = "FD")
lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
rug(x)
}
if (histogram)
pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor,
diag.panel = hist.panel, ...)
else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}
chart.Correlation.new(AEDB.CEA.matrix.serum.RANK, method = "spearman", histogram = TRUE, pch = 3)
# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")
# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables
library(GGally)
# From the help page:
ggpairs(AEDB.CEA,
columns = c("MCP1_rank", TRAITS.BIN, TRAITS.CON.RANK, "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite"),
columnLabels = c("MCP1 (serum)",
"Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density",
"Symptoms", "Symptoms (grouped)", "MACE", "Composite"),
method = c("spearman"),
# ggplot2::aes(colour = Gender),
progress = FALSE) Extra arguments: 'method' are being ignored. If these are meant to be aesthetics, submit them using the 'mapping' variable within ggpairs with ggplot2::aes or ggplot2::aes_string.
Additional figures
Age and sex
We want to create per-age-group figures.
- Box and Whisker plot for MCP-1 plaque levels by sex and age group (<55, 55-64, 65-74, 75-84, 85+)
- Box and Whisker plot for MCP-1 serum levels by sex and age group (<55, 55-64, 65-74, 75-84, 85+)
- Scatter plot of the correlation and regression line between MCP-1 levels in plaque (y axis) and serum (x axis).
library(dplyr)
AEDB.CEA <- AEDB.CEA %>% mutate(AgeGroup = case_when(Age < 55 ~ "<55",
Age >= 55 & Age <= 64 ~ "55-64",
Age >= 65 & Age <= 74 ~ "65-74",
Age >= 75 & Age <= 84 ~ "75-84",
Age >= 85 ~ "85+"))
AEDB.CEA <- AEDB.CEA %>% mutate(AgeGroupSex = case_when(Age < 55 & Gender == "male" ~ "<55 males" ,
Age >= 55 & Age <= 64 & Gender == "male"~ "55-64 males",
Age >= 65 & Age <= 74 & Gender == "male"~ "65-74 males",
Age >= 75 & Age <= 84 & Gender == "male"~ "75-84 males",
Age >= 85 & Gender == "male"~ "85+ males",
Age < 55 & Gender == "female" ~ "<55 females" ,
Age >= 55 & Age <= 64 & Gender == "female"~ "55-64 females ",
Age >= 65 & Age <= 74 & Gender == "female"~ "65-74 females",
Age >= 75 & Age <= 84 & Gender == "female"~ "75-84 females",
Age >= 85 & Gender == "female"~ "85+ females"))
table(AEDB.CEA$AgeGroup, AEDB.CEA$Gender)
female male
<55 45 98
55-64 193 410
65-74 264 687
75-84 202 438
85+ 34 50table(AEDB.CEA$AgeGroupSex)
<55 females <55 males 55-64 females 55-64 males 65-74 females 65-74 males 75-84 females 75-84 males 85+ females 85+ males
45 98 193 410 264 687 202 438 34 50 Now we can draw some graphs of serum/plaque MCP1 levels per sex and age group.
# ?ggpubr::ggboxplot()
ggpubr::ggboxplot(AEDB.CEA,
x = c("Gender"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"))
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Age groups (years) per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"))
ggpubr::ggboxplot(AEDB.CEA,
x = c("Gender"),
y = "MCP1_rank",
xlab = "Gender",
ylab = "MCP1 serum [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"))
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1_rank",
xlab = "Age groups (years) per gender",
ylab = "MCP1 serum [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"))
Simalarly but now for the raw data as median ± interquartile range.
# ?ggpubr::ggboxplot()
ggpubr::ggboxplot(AEDB.CEA,
x = c("Gender"),
y = "MCP1_pg_ug_2015",
xlab = "Gender",
ylab = "MCP1 plaque [pg/ug]",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "median_iqr")
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1_pg_ug_2015",
xlab = "Age groups (years) per gender",
ylab = "MCP1 plaque [pg/ug]",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "median_iqr")
ggpubr::ggboxplot(AEDB.CEA,
x = c("Gender"),
y = "MCP1",
xlab = "Gender",
ylab = "MCP1 serum [pg/mL]",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "median_iqr")
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1",
xlab = "Age groups (years) per gender",
ylab = "MCP1 serum [pg/mL]",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "median_iqr")We will also make a nice correlation plot between serum and plaque MCP1 levels.
ggpubr::ggscatter(AEDB.CEA,
x = "MCP1_pg_ug_2015",
y = "MCP1",
xlab = "MCP1 plaque [pg/ug]",
ylab = "MCP1 serum [pg/mL]",
add = "reg.line", add.params = list(color = "#1290D9"),
conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method = "spearman"), cor.coef.coord = c(8,750))
ggpubr::ggscatter(AEDB.CEA,
x = "MCP1_pg_ug_2015_rank",
y = "MCP1_rank",
xlab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
ylab = "MCP1 serum [pg/mL]\n(inverse-rank transformation)",
add = "reg.line", add.params = list(color = "#1290D9"),
conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method = "spearman"), cor.coef.coord = c(2,3))
Symptoms
We want to create per-symptom figures.
library(dplyr)
table(AEDB.CEA$AgeGroup, AEDB.CEA$AsymptSympt2G)
Asymptomatic Symptomatic
<55 24 119
55-64 76 527
65-74 124 827
75-84 43 597
85+ 3 81table(AEDB.CEA$Gender, AEDB.CEA$AsymptSympt2G)
Asymptomatic Symptomatic
female 64 674
male 206 1477table(AEDB.CEA$AsymptSympt2G)
Asymptomatic Symptomatic
270 2151 Now we can draw some graphs of serum/plaque MCP1 levels per symptom group.
# ?ggpubr::ggboxplot()
my_comparisons <- list(c("Asymptomatic", "Symptomatic"))
p1 <- ggpubr::ggboxplot(AEDB.CEA,
x = "AsymptSympt2G", y = "MCP1_pg_ug_2015_rank",
title = "MCP1 plaque [pg/ug] levels per symptom",
xlab = "Symptoms",
ylab = "MCP1 plaque [pg/ug]\n inverse-rank transformation",
color = "AsymptSympt2G",
palette = c(uithof_color[16], uithof_color[23]),
add = "dotplot", # Add dotplot
add.params = list(binwidth = 0.1, dotsize = 0.3)
) +
stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")
p1 <- ggpubr::ggboxplot(AEDB.CEA,
x = "AsymptSympt2G", y = "MCP1_rank",
title = "MCP1 serum [pg/mL] levels per symptom",
xlab = "Symptoms",
ylab = "MCP1 serum [pg/mL]\n inverse-rank transformation",
color = "AsymptSympt2G",
palette = c(uithof_color[16], uithof_color[23]),
add = "dotplot", # Add dotplot
add.params = list(binwidth = 0.1, dotsize = 0.3)
) +
stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")
rm(p1)We would also like to visualize the multivariable analyses results.
library(ggplot2)
model1_mcp1 <- read.xlsx(paste0(OUT_loc, "/", Today, ".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"))
model2_mcp1 <- read.xlsx(paste0(OUT_loc, "/", Today, ".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"))
model1_mcp1$model <- "univariate"
model2_mcp1$model <- "multivariate"
models_mcp1 <- rbind(model1_mcp1, model2_mcp1)
models_mcp1NAdat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"),
levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
cen = c(models_mcp1$OR[models_mcp1$Predictor=="MCP1_pg_ug_2015_rank"]),
low = c(models_mcp1$low95CI[models_mcp1$Predictor=="MCP1_pg_ug_2015_rank"]),
high = c(models_mcp1$up95CI[models_mcp1$Predictor=="MCP1_pg_ug_2015_rank"]))
fp <- ggplot(data=dat, aes(x=group, y=cen, ymin=low, ymax=high)) +
geom_pointrange() +
geom_hline(yintercept=1, lty=2) + # add a dotted line at x=1 after flip
coord_flip() + # flip coordinates (puts labels on y axis)
xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
theme(text = element_text(size=14)) +
ggtitle("Plaque MCP-1 levels (1 SD increment)") +
theme_minimal() # use a white background
print(fp)
rm(fp)dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"),
levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
cen = c(models_mcp1$OR[models_mcp1$Predictor=="MCP1_rank"]),
low = c(models_mcp1$low95CI[models_mcp1$Predictor=="MCP1_rank"]),
high = c(models_mcp1$up95CI[models_mcp1$Predictor=="MCP1_rank"]))
fp <- ggplot(data=dat, aes(x=group, y=cen, ymin=low, ymax=high)) +
geom_pointrange() +
geom_hline(yintercept=1, lty=2) + # add a dotted line at x=1 after flip
coord_flip() + # flip coordinates (puts labels on y axis)
xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
theme(text = element_text(size=14)) +
ggtitle("Serum MCP-1 levels (1 SD increment)") +
theme_minimal() # use a white background
print(fp)
rm(fp)Forest plots in R (ggplot) with side table
A friend asked me to help with a forest plot recently. After chatting about what she wanted the end result to look like, this is what I came up with.
data_table | p
Error: Aesthetics must be either length 1 or the same as the data (28): yintercept
Some other options for forest plots included this:
From Abhijit blog. But I figured I’d start a fresh, and I’d already borrowed some code of Abhijits in the past for a survival curve.
The package rmeta offers two different versions. One with forestplot like so:
And one with metaplot like so:
Ultimately the two would be used for different purposes, and these examples shown are straight from the manual for rmeta.
It wasn’t too much work to create ‘another’ custom option to get what my friend wanted, so thought I’d share the code for anyone else interest. There was a little bit of ‘misdirection’ for lack of a better term to get it across the line, and I’m sure elements of this are sloppy. You can access the code on my github.
I wrote this post in RStudio using the R Markdown language and then knitr to turn in into markdown (.md), and then pandoc to turn it into html. The original file is available here on github.
system(“pandoc -s forest_plot.md -o forest_plot.html”)
Session information
Version: v1.0.6
Last update: 2020-06-16
Written by: Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
Description: Script to analyse MCP1 from the Ather-Express Biobank Study.
Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).
Changes log
* v1.0.6 Only analyses and figures that end up in the final manuscript.
* v1.0.5 Update with 30- and 90-days survival.
* v1.0.4 Updated with Cox-regressions.
* v1.0.3 Included more models.
* v1.0.2 Bugs fixed.
* v1.0.1 Extended with linear and logistic regressions
* v1.0.0 Inital versionsessionInfo()R version 3.6.3 (2020-02-29)
Platform: x86_64-apple-darwin19.4.0 (64-bit)
Running under: macOS Catalina 10.15.5
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /usr/local/Cellar/openblas/0.3.9/lib/libopenblasp-r0.3.9.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] tools stats graphics grDevices utils datasets methods base
other attached packages:
[1] rmeta_3.0 gridExtra_2.3 patchwork_1.0.0.9000 hablar_0.3.0 sjPlot_2.8.4 sjlabelled_1.1.5
[7] GGally_1.5.0 PerformanceAnalytics_2.0.4 xts_0.12-0 zoo_1.8-8 ggcorrplot_0.1.3.999 Hmisc_4.4-0
[13] Formula_1.2-3 lattice_0.20-41 survminer_0.4.6 survival_3.1-12 labelled_2.4.0 openxlsx_4.1.5
[19] ggpubr_0.3.0 tableone_0.11.1 haven_2.3.0 devtools_2.3.0 usethis_1.6.1 MASS_7.3-51.6
[25] DT_0.13 knitr_1.28 forcats_0.5.0 stringr_1.4.0 purrr_0.3.4 tibble_3.0.1
[31] ggplot2_3.3.0 tidyverse_1.3.0 data.table_1.12.8 naniar_0.5.1 tidyr_1.1.0 dplyr_0.8.5
[37] optparse_1.6.6 readr_1.3.1
loaded via a namespace (and not attached):
[1] tidyselect_1.1.0 lme4_1.1-23 htmlwidgets_1.5.1 grid_3.6.3 munsell_0.5.0 codetools_0.2-16 effectsize_0.3.1 statmod_1.4.34 withr_2.2.0
[10] colorspace_1.4-1 highr_0.8 rstudioapi_0.11 stats4_3.6.3 ggsignif_0.6.0 labeling_0.3 emmeans_1.4.7 rstan_2.19.3 KMsurv_0.1-5
[19] farver_2.0.3 rprojroot_1.3-2 coda_0.19-3 vctrs_0.3.0 generics_0.0.2 TH.data_1.0-10 xfun_0.14 R6_2.4.1 reshape_0.8.8
[28] assertthat_0.2.1 scales_1.1.1 multcomp_1.4-13 nnet_7.3-14 gtable_0.3.0 processx_3.4.2 sandwich_2.5-1 rlang_0.4.6 splines_3.6.3
[37] rstatix_0.5.0.999 acepack_1.4.1 broom_0.5.6 checkmate_2.0.0 inline_0.3.15 yaml_2.2.1 reshape2_1.4.4 abind_1.4-5 modelr_0.1.8
[46] crosstalk_1.1.0.1 backports_1.1.7 rsconnect_0.8.16 ellipsis_0.3.1 RColorBrewer_1.1-2 sessioninfo_1.1.1 Rcpp_1.0.4.6 plyr_1.8.6 base64enc_0.1-3
[55] ps_1.3.3 prettyunits_1.1.1 rpart_4.1-15 cluster_2.1.0 fs_1.4.1 survey_4.0 magrittr_1.5 reprex_0.3.0 mvtnorm_1.1-0
[64] packrat_0.5.0 sjmisc_2.8.4 matrixStats_0.56.0 pkgload_1.0.2 hms_0.5.3 evaluate_0.14 xtable_1.8-4 rio_0.5.16 sjstats_0.18.0
[73] jpeg_0.1-8.1 readxl_1.3.1 ggeffects_0.14.3 testthat_2.3.2 compiler_3.6.3 crayon_1.3.4 minqa_1.2.4 StanHeaders_2.19.2 htmltools_0.4.0
[82] mgcv_1.8-31 visdat_0.5.3 lubridate_1.7.8 DBI_1.1.0 dbplyr_1.4.3 boot_1.3-25 Matrix_1.2-18 getopt_1.20.3 car_3.0-8
[91] cli_2.0.2 mitools_2.4 quadprog_1.5-8 parallel_3.6.3 insight_0.8.4 pkgconfig_2.0.3 km.ci_0.5-2 foreign_0.8-75 xml2_1.3.2
[100] estimability_1.3 rvest_0.3.5 callr_3.4.3 digest_0.6.25 parameters_0.7.0 rmarkdown_2.1 cellranger_1.1.0 survMisc_0.5.5 htmlTable_1.13.3
[109] curl_4.3 nloptr_1.2.2.1 lifecycle_0.2.0 nlme_3.1-148 jsonlite_1.6.1 carData_3.0-4 desc_1.2.0 fansi_0.4.1 pillar_1.4.4
[118] ggsci_2.9 loo_2.2.0 httr_1.4.1 pkgbuild_1.0.8 glue_1.4.1 remotes_2.1.1 bayestestR_0.6.0 zip_2.0.4 png_0.1-7
[127] class_7.3-17 stringi_1.4.6 performance_0.4.6 latticeExtra_0.6-29 memoise_1.1.0 e1071_1.7-3 Saving environment
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".sample_selection.RData"))| © 1979-2020 Sander W. van der Laan | s.w.vanderlaan-2[at]umcutrecht.nl | swvanderlaan.github.io. |
---
title: "Athero-Express Biobank Study -- IL6 and MCP1 plaque levels."
author: '[Sander W. van der Laan, PhD](https://swvanderlaan.github.io) | @swvanderlaan; Marios Georgakis; Rainer Malik; Martin Dichgans'
date: '`r Sys.Date()`'
output:
  html_notebook:
    cache: yes
    code_folding: hide
    collapse: yes
    df_print: paged
    fig.align: center
    fig_caption: yes
    fig_height: 10
    fig_retina: 2
    fig_width: 12
    theme: paper
    toc: yes
    toc_float:
      collapsed: no
      smooth_scroll: yes
mainfont: Helvetica
subtitle: An 'Athero-Express Biobank Study' project
editor_options:
  chunk_output_type: inline
---
```{r global_options, include = FALSE}
# further define some knitr-options.
knitr::opts_chunk$set(fig.width = 12, fig.height = 8, fig.path = 'Figures/',
                      eval = TRUE, warning = FALSE, message = FALSE)
```

# Preparation

Clean the environment.
```{r ClearEnvironment, include = FALSE}
rm(list = ls())
```

Set locations, and the working directory.
```{r LocalSystem, include = FALSE}
### Operating System Version
### Mac Pro
# ROOT_loc = "/Volumes/EliteProQx2Media"
# GENOMIC_loc = "/Users/svanderlaan/iCloud/Genomics"

### MacBook
ROOT_loc = "/Users/swvanderlaan"
GENOMIC_loc = paste0(ROOT_loc, "/iCloud/Genomics")

### Generic Locations
AEDB_loc = paste0(GENOMIC_loc, "/AE-AAA_GS_DBs")
LAB_loc = paste0(GENOMIC_loc, "/LabBusiness")
RESULTS = paste0(ROOT_loc, "/PLINK/analyses/lookups/AE_20190912_010_MDICHGANS_SWVDLAAN_IL6_MCP1")
RAWDATA = paste0(ROOT_loc, "/PLINK/_AE_ORIGINALS/AESCRNA/prepped_data")
PROJECT_loc = paste0(ROOT_loc, "/PLINK/analyses/lookups/AE_20190912_010_MDICHGANS_SWVDLAAN_IL6_MCP1")

### SOME VARIABLES WE NEED DOWN THE LINE
cat("\nDefining phenotypes and datasets.\n")
PROJECTNAME="IL6MCP1"
# SUBPROJECTNAME=""

cat("\nCreate a new analysis directory, including subdirectories.\n")
# Analysis
ifelse(!dir.exists(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       dir.create(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       FALSE)
ANALYSIS_loc = paste0(PROJECT_loc,"/",PROJECTNAME)

# Plots
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/PLOTS")), 
       dir.create(file.path(ANALYSIS_loc, "/PLOTS")), 
       FALSE)
PLOT_loc = paste0(ANALYSIS_loc,"/PLOTS")

# QC plots
ifelse(!dir.exists(file.path(PLOT_loc, "/QC")), 
       dir.create(file.path(PLOT_loc, "/QC")), 
       FALSE)
QC_loc = paste0(PLOT_loc,"/QC")

# Output files
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/OUTPUT")), 
       dir.create(file.path(ANALYSIS_loc, "/OUTPUT")), 
       FALSE)
OUT_loc = paste0(ANALYSIS_loc, "/OUTPUT")

# COX analysis
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/COX")), 
       dir.create(file.path(ANALYSIS_loc, "/COX")), 
       FALSE)
COX_loc = paste0(ANALYSIS_loc, "/COX")

# Baseline characteristics
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/BASELINE")), 
       dir.create(file.path(ANALYSIS_loc, "/BASELINE")), 
       FALSE)
BASELINE_loc = paste0(ANALYSIS_loc, "/BASELINE")

# Sample selection
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/SELECTIONS")), 
       dir.create(file.path(ANALYSIS_loc, "/SELECTIONS")), 
       FALSE)
SELECTIONS_loc = paste0(ANALYSIS_loc, "/SELECTIONS")

cat("\nSetting working directory and listing its contents.\n")
setwd(paste0(PROJECT_loc))
getwd()
list.files()
```

A package-installation function.
```{r Function: installations, include = FALSE}
install.packages.auto <- function(x) { 
  x <- as.character(substitute(x)) 
  if(isTRUE(x %in% .packages(all.available = TRUE))) { 
    eval(parse(text = sprintf("require(\"%s\")", x)))
  } else { 
    # Update installed packages - this may mean a full upgrade of R, which in turn
    # may not be warrented. 
    # update.install.packages.auto(ask = FALSE) 
    eval(parse(text = sprintf("install.packages(\"%s\", dependencies = TRUE, repos = \"https://cloud.r-project.org/\")", x)))
  }
  if(isTRUE(x %in% .packages(all.available = TRUE))) { 
    eval(parse(text = sprintf("require(\"%s\")", x)))
  } else {
    if (!requireNamespace("BiocManager"))
      install.packages("BiocManager")
    # BiocManager::install() # this would entail updating installed packages, which in turned may not be warrented
    eval(parse(text = sprintf("BiocManager::install(\"%s\")", x)))
    eval(parse(text = sprintf("require(\"%s\")", x)))
  }
}
```

Load those packages.
```{r Setting: loading_packages, include = FALSE}
install.packages.auto("readr")
install.packages.auto("optparse")
install.packages.auto("tools")
install.packages.auto("dplyr")
install.packages.auto("tidyr")
install.packages.auto("naniar")

# To get 'data.table' with 'fwrite' to be able to directly write gzipped-files
# Ref: https://stackoverflow.com/questions/42788401/is-possible-to-use-fwrite-from-data-table-with-gzfile
# install.packages("data.table", repos = "https://Rdatatable.gitlab.io/data.table")
library(data.table)

install.packages.auto("tidyverse")
install.packages.auto("knitr")
install.packages.auto("DT")
install.packages.auto("MASS")
# install.packages.auto("Seurat") # latest version

# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')

install.packages.auto("haven")
install.packages.auto("sjlabelled")
install.packages.auto("sjPlot")
install.packages.auto("labelled")
install.packages.auto("tableone")

install.packages.auto("ggpubr")

```

We will create a datestamp and define the Utrecht Science Park Colour Scheme.
```{r Setting: Colors, include = FALSE}

Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")

### UtrechtScienceParkColoursScheme
###
### WebsitetoconvertHEXtoRGB:http://hex.colorrrs.com.
### Forsomefunctionsyoushoulddividethesenumbersby255.
### 
###	No.	Color			      HEX	(RGB)						              CHR		  MAF/INFO
###---------------------------------------------------------------------------------------
###	1	  yellow			    #FBB820 (251,184,32)				      =>	1		or 1.0>INFO
###	2	  gold			      #F59D10 (245,157,16)				      =>	2		
###	3	  salmon			    #E55738 (229,87,56)				      =>	3		or 0.05<MAF<0.2 or 0.4<INFO<0.6
###	4	  darkpink		    #DB003F ((219,0,63)				      =>	4		
###	5	  lightpink		    #E35493 (227,84,147)				      =>	5		or 0.8<INFO<1.0
###	6	  pink			      #D5267B (213,38,123)				      =>	6		
###	7	  hardpink		    #CC0071 (204,0,113)				      =>	7		
###	8	  lightpurple	    #A8448A (168,68,138)				      =>	8		
###	9	  purple			    #9A3480 (154,52,128)				      =>	9		
###	10	lavendel		    #8D5B9A (141,91,154)				      =>	10		
###	11	bluepurple		  #705296 (112,82,150)				      =>	11		
###	12	purpleblue		  #686AA9 (104,106,169)			      =>	12		
###	13	lightpurpleblue	#6173AD (97,115,173/101,120,180)	=>	13		
###	14	seablue			    #4C81BF (76,129,191)				      =>	14		
###	15	skyblue			    #2F8BC9 (47,139,201)				      =>	15		
###	16	azurblue		    #1290D9 (18,144,217)				      =>	16		or 0.01<MAF<0.05 or 0.2<INFO<0.4
###	17	lightazurblue	  #1396D8 (19,150,216)				      =>	17		
###	18	greenblue		    #15A6C1 (21,166,193)				      =>	18		
###	19	seaweedgreen	  #5EB17F (94,177,127)				      =>	19		
###	20	yellowgreen		  #86B833 (134,184,51)				      =>	20		
###	21	lightmossgreen	#C5D220 (197,210,32)				      =>	21		
###	22	mossgreen		    #9FC228 (159,194,40)				      =>	22		or MAF>0.20 or 0.6<INFO<0.8
###	23	lightgreen	  	#78B113 (120,177,19)				      =>	23/X
###	24	green			      #49A01D (73,160,29)				      =>	24/Y
###	25	grey			      #595A5C (89,90,92)				        =>	25/XY	or MAF<0.01 or 0.0<INFO<0.2
###	26	lightgrey		    #A2A3A4	(162,163,164)			      =>	26/MT
###
###	ADDITIONAL COLORS
###	27	midgrey			#D7D8D7
###	28	verylightgrey	#ECECEC"
###	29	white			#FFFFFF
###	30	black			#000000
###----------------------------------------------------------------------------------------------

uithof_color = c("#FBB820","#F59D10","#E55738","#DB003F","#E35493","#D5267B",
                 "#CC0071","#A8448A","#9A3480","#8D5B9A","#705296","#686AA9",
                 "#6173AD","#4C81BF","#2F8BC9","#1290D9","#1396D8","#15A6C1",
                 "#5EB17F","#86B833","#C5D220","#9FC228","#78B113","#49A01D",
                 "#595A5C","#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

uithof_color_legend = c("#FBB820", "#F59D10", "#E55738", "#DB003F", "#E35493",
                        "#D5267B", "#CC0071", "#A8448A", "#9A3480", "#8D5B9A",
                        "#705296", "#686AA9", "#6173AD", "#4C81BF", "#2F8BC9",
                        "#1290D9", "#1396D8", "#15A6C1", "#5EB17F", "#86B833",
                        "#C5D220", "#9FC228", "#78B113", "#49A01D", "#595A5C",
                        "#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

#ggplot2 default color palette
gg_color_hue <- function(n) {
  hues = seq(15, 375, length = n + 1)
  hcl(h = hues, l = 65, c = 100)[1:n]
}

### ----------------------------------------------------------------------------
```


```{r Analysis Functions}
# Function to grep data from glm()/lm()
GLM.CON <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
  cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' .\n")
  if (nrow(summary(fit)$coefficients) == 1) {
    output = c(DATASET, x_name, y, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    effectsize = summary(fit)$coefficients[2,1]
    SE = summary(fit)$coefficients[2,2]
    OReffect = exp(summary(fit)$coefficients[2,1])
    CI_low = exp(effectsize - 1.96 * SE)
    CI_up = exp(effectsize + 1.96 * SE)
    tvalue = summary(fit)$coefficients[2,3]
    pvalue = summary(fit)$coefficients[2,4]
    R = summary(fit)$r.squared
    R.adj = summary(fit)$adj.r.squared
    sample_size = nrow(model.frame(fit))
    AE_N = AEDB.CEA.samplesize
    Perc_Miss = 100 - ((sample_size * 100)/AE_N)
    
    output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, tvalue, pvalue, R, R.adj, AE_N, sample_size, Perc_Miss)
    
    if (verbose == TRUE) {
    cat("We have collected the following and summarize it in an object:\n")
    cat("Dataset...................:", DATASET, "\n")
    cat("Score/Exposure/biomarker..:", x_name, "\n")
    cat("Trait/outcome.............:", y, "\n")
    cat("Effect size...............:", round(effectsize, 6), "\n")
    cat("Standard error............:", round(SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
    cat("T-value...................:", round(tvalue, 6), "\n")
    cat("P-value...................:", signif(pvalue, 8), "\n")
    cat("R^2.......................:", round(R, 6), "\n")
    cat("Adjusted r^2..............:", round(R.adj, 6), "\n")
    cat("Sample size of AE DB......:", AE_N, "\n")
    cat("Sample size of model......:", sample_size, "\n")
    cat("Missing data %............:", round(Perc_Miss, 6), "\n")
    } else {
      cat("Collecting data in summary object.\n")
    }
  }
  return(output)
  print(output)
}

GLM.BIN <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
  cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' ...\n")
  if (nrow(summary(fit)$coefficients) == 1) {
    output = c(DATASET, x_name, y, rep(NA,9))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data...\n")
    effectsize = summary(fit)$coefficients[2,1]
    SE = summary(fit)$coefficients[2,2]
    OReffect = exp(summary(fit)$coefficients[2,1])
    CI_low = exp(effectsize - 1.96 * SE)
    CI_up = exp(effectsize + 1.96 * SE)
    zvalue = summary(fit)$coefficients[2,3]
    pvalue = summary(fit)$coefficients[2,4]
    dev <- fit$deviance
    nullDev <- fit$null.deviance
    modelN <- length(fit$fitted.values)
    R.l <- 1 - dev / nullDev
    R.cs <- 1 - exp(-(nullDev - dev) / modelN)
    R.n <- R.cs / (1 - (exp(-nullDev/modelN)))
    sample_size = nrow(model.frame(fit))
    AE_N = AEDB.CEA.samplesize
    Perc_Miss = 100 - ((sample_size * 100)/AE_N)
    
    output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, zvalue, pvalue, R.l, R.cs, R.n, AE_N, sample_size, Perc_Miss)
    if (verbose == TRUE) {
    cat("We have collected the following and summarize it in an object:\n")
    cat("Dataset...................:", DATASET, "\n")
    cat("Score/Exposure/biomarker..:", x_name, "\n")
    cat("Trait/outcome.............:", y, "\n")
    cat("Effect size...............:", round(effectsize, 6), "\n")
    cat("Standard error............:", round(SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
    cat("Z-value...................:", round(zvalue, 6), "\n")
    cat("P-value...................:", signif(pvalue, 8), "\n")
    cat("Hosmer and Lemeshow r^2...:", round(R.l, 6), "\n")
    cat("Cox and Snell r^2.........:", round(R.cs, 6), "\n")
    cat("Nagelkerke's pseudo r^2...:", round(R.n, 6), "\n")
    cat("Sample size of AE DB......:", AE_N, "\n")
    cat("Sample size of model......:", sample_size, "\n")
    cat("Missing data %............:", round(Perc_Miss, 6), "\n")
    } else {
      cat("Collecting data in summary object.\n")
    }
  }
  return(output)
  print(output)
}
```


# Background

Using a Mendelian Randomization approach, we recently examined associations between the circulating levels of 41 cytokines and growth factors and the risk of stroke in the MEGASTROKE GWAS dataset (67,000 stroke cases and 450,000 controls) and found Monocyte chemoattractant protein-1 (MCP-1) as the cytokine showing the strongest association with stroke, particularly large artery and cardioembolic stroke (Georgakis et al., 2019a). Genetically elevated MCP-1 levels were also associated with a higher risk of coronary artery disease and myocardial infarction (Georgakis et al., 2019a). Further, in a meta-analysis of 6 observational population-based of longitudinal cohort studies we recently showed that baseline levels of MCP-1 were associated with a higher risk of ischemic stroke over follow-up (Georgakis et al., 2019b).
While these data suggest a central role of MCP-1 in the pathogenesis of atherosclerosis, it remains unknown if MCP-1 levels in the blood really reflect MCP-1 activity. MCP-1 is expressed in the atherosclerotic plaque and attracts monocytes in the subendothelial space (Nelken et al., 1991; Papadopoulou et al., 2008; Takeya et al., 1993; Wilcox et al., 1994). Thus, MCP-1 levels in the plaque might more strongly reflect MCP-1 signaling. However, it remains unknown if MCP-1 plaque levels associate with plaque vulnerability or risk of cardiovascular events.


## Objectives

Against this background we now aim to make use of the data from Athero-Express Biobank Study to explore the associations of MCP-1 protein levels in the atherosclerotic plaques from patients undergoing carotid endarterectomy with phenotypes of plaque vulnerability and secondary vascular events over a follow-up of three years.


## Methods

*Blood*

- IL6: Interleukin 6. Entrez Gene: 3569. Bender MedSystems; cat.nr.: BMS810FF. Recalculated FACS. [pg/mL]
- MCP1: Monocyte chemotactic protein 1, MCP-1 (Chemokine (C-C motif) ligand 2, CCL2). Entrez Gene: 6347. Measured at the WKZ. Recalculated Luminex. [pg/mL]


*Plaque*

- IL6: Interleuking 6 (IL6; Entrez Gene: 3569) concentration in plaque [pg/ug], measured by Luminex at the WKZ.
- MCP1: Monocyte chemotactic protein 1 (a.k.a. CCL2; Entrez Gene: 6347) concentration in plaque [pg/ug], measured by Luminex at the WKZ.
- IL6R: Interleuking 6 receptor (IL6R; Entrez Gene: 3570) concentration in plaque [pg/ug], measured by Luminex at the WKZ.


# Loading data

## Clinical data

Loading Athero-Express clinical data.
```{r LoadAEDB}
require(haven)

# AEDB <- haven::read_sav(paste0(AEDB_loc, "/2019-3NEW_AtheroExpressDatabase_ScientificAE_02072019_IC_added.sav"))
AEDB <- haven::read_sav(paste0(AEDB_loc, "/2020_1_NEW_AtheroExpressDatabase_ScientificAE_16-03-2020.sav"))

head(AEDB)



```

#### Examine AEDB

We can examine the contents of the Athero-Express Biobank dataset to know what each variable is called, what class (type) it has, and what the variable description is. 

There is an excellent post on this: https://www.r-bloggers.com/working-with-spss-labels-in-r/. 
```{r AEDB: describe}
AEDB %>% sjPlot::view_df(show.type = TRUE,
                         show.frq = TRUE,
                         show.prc = TRUE,
                         show.na = TRUE, 
                         max.len = TRUE, 
                         wrap.labels = 20,
                         verbose = FALSE, 
                         use.viewer = FALSE,
                         file = paste0(OUT_loc, "/", Today, ".AEDB.dictionary.html")) 
```


## Fixing and creating variables

We need to be very strict in defining _symptoms._ Therefore we will fix a new variable that groups _symptoms_ at inclusion.

Coding of _symptoms_ is as follows:

- missing	-999	
- Asymptomatic	0	
- TIA	1	
- minor stroke	2	
- Major stroke	3	
- Amaurosis fugax	4	
- Four vessel disease	5	
- Vertebrobasilary TIA	7	
- Retinal infarction	8	
- Symptomatic, but aspecific symtoms	9
- Contralateral symptomatic occlusion	10	
- retinal infarction	11	
- armclaudication due to occlusion subclavian artery, CEA needed for bypass	12	
- retinal infarction + TIAs	13	
- Ocular ischemic syndrome	14	
- ischemisch glaucoom	15	
- subclavian steal syndrome	16	
- TGA	17

We will group as follows in `Symptoms.5G`:

1. Asymptomatic > 0
2. TIA > 1, 7, 13
3. Stroke > 2, 3
4. Ocular > 4, 14, 15
5. Retinal infarction > 8, 11
6. Other > 5, 9, 10, 12, 16, 17

We will also group as follows in `AsymptSympt`:

1. Asymptomatic > 0
2. TIA > 1, 7, 13 + Stroke > 2, 3 
3. Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17

We will also group as follows in `AsymptSympt2G`:

1. Asymptomatic > 0
2. TIA > 1, 7, 13 + Stroke > 2, 3 Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17


```{r FixSymptoms, message=FALSE, warning=FALSE}
# Fix symptoms

attach(AEDB)

AEDB$sympt[is.na(AEDB$sympt)] <- -999

# Symptoms.5G
AEDB[,"Symptoms.5G"] <- NA
# AEDB$Symptoms.5G[sympt == "NA"] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == -999] <- NA
AEDB$Symptoms.5G[sympt == 0] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == 1 | sympt == 7 | sympt == 13] <- "TIA"
AEDB$Symptoms.5G[sympt == 2 | sympt == 3] <- "Stroke"
AEDB$Symptoms.5G[sympt == 4 | sympt == 14 | sympt == 15 ] <- "Ocular"
AEDB$Symptoms.5G[sympt == 8 | sympt == 11] <- "Retinal infarction"
AEDB$Symptoms.5G[sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Other"

# AsymptSympt
AEDB[,"AsymptSympt"] <- NA
AEDB$AsymptSympt[sympt == -999] <- NA
AEDB$AsymptSympt[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3] <- "Symptomatic"
AEDB$AsymptSympt[sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Ocular and others"

# AsymptSympt
AEDB[,"AsymptSympt2G"] <- NA
AEDB$AsymptSympt2G[sympt == -999] <- NA
AEDB$AsymptSympt2G[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt2G[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3 | sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Symptomatic"

detach(AEDB)

# table(AEDB$sympt, useNA = "ifany")
# table(AEDB$AsymptSympt2G, useNA = "ifany")
# table(AEDB$Symptoms.5G, useNA = "ifany")
# 
# table(AEDB$AsymptSympt2G, AEDB$sympt, useNA = "ifany")
# table(AEDB$Symptoms.5G, AEDB$sympt, useNA = "ifany")
table(AEDB$AsymptSympt2G, AEDB$Symptoms.5G, useNA = "ifany")

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "sympt", "Symptoms.5G", "AsymptSympt"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# table(AEDB.temp$Symptoms.5G, AEDB.temp$AsymptSympt)
# 
# rm(AEDB.temp)

```

We will also fix the _plaquephenotypes_ variable.  

Coding of symptoms is as follows:

- missing	-999	
- not relevant -888
- fibrous	1	
- fibroatheromatous	2	
- atheromatous	3	


```{r FixPlaquePhenotypes, message=FALSE, warning=FALSE}

# Fix plaquephenotypes
attach(AEDB)
AEDB[,"OverallPlaquePhenotype"] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == 1] <- "fibrous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 2] <- "fibroatheromatous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 3] <- "atheromatous"
detach(AEDB)

table(AEDB$OverallPlaquePhenotype)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "plaquephenotype", "OverallPlaquePhenotype"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

```

We will also fix the _diabetes_ status variable. We define diabetes as history of a diagnosis and/or use of glucose-lowering medications.

```{r FixDiabetes, message=FALSE, warning=FALSE}
# Fix diabetes
attach(AEDB)
AEDB[,"DiabetesStatus"] <- NA
AEDB$DiabetesStatus[DM.composite == -999] <- NA
AEDB$DiabetesStatus[DM.composite == 0] <- "Control (no Diabetes Dx/Med)"
AEDB$DiabetesStatus[DM.composite == 1] <- "Diabetes"
detach(AEDB)

table(AEDB$DM.composite)

table(AEDB$DiabetesStatus)


# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

```


We will also fix the _smoking_ status variable. We are interested in whether someone never, ever or is currently (at the time of inclusion) smoking. This is based on the questionnaire. 

- `diet801`: are you a smoker?
- `diet802`: did you smoke in the past?

We already have some variables indicating smoking status:

- `SmokingReported`: patient has reported to smoke.
- `SmokingYearOR`: smoking in the year of surgery?
- `SmokerCurrent`: currently smoking?



```{r FixSmoking, message=FALSE, warning=FALSE}
require(labelled)
AEDB$diet801 <- to_factor(AEDB$diet801)
AEDB$diet802 <- to_factor(AEDB$diet802)
AEDB$diet805 <- to_factor(AEDB$diet805)
AEDB$SmokingReported <- to_factor(AEDB$SmokingReported)
AEDB$SmokerCurrent <- to_factor(AEDB$SmokerCurrent)
AEDB$SmokingYearOR <- to_factor(AEDB$SmokingYearOR)

# table(AEDB$diet801)
# table(AEDB$diet802)
# table(AEDB$SmokingReported)
# table(AEDB$SmokerCurrent)
# table(AEDB$SmokingYearOR)
# table(AEDB$SmokingReported, AEDB$SmokerCurrent, useNA = "ifany", dnn = c("Reported smoking", "Current smoker"))
# 
# table(AEDB$diet801, AEDB$diet802, useNA = "ifany", dnn = c("Smoker", "Past smoker"))

cat("\nFixing smoking status.\n")
attach(AEDB)
AEDB[,"SmokerStatus"] <- NA
AEDB$SmokerStatus[diet802 == "don't know"] <- "Never smoked"
AEDB$SmokerStatus[diet802 == "I still smoke"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "no"] <- "Never smoked"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "yes"] <- "Ex-smoker"
AEDB$SmokerStatus[SmokerCurrent == "yes"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no data available/missing"] <- NA
# AEDB$SmokerStatus[is.na(SmokerCurrent)] <- "Never smoked"
detach(AEDB)

cat("\n* Current smoking status.\n")
table(AEDB$SmokerCurrent,
      useNA = "ifany", 
      dnn = c("Current smoker"))

cat("\n* Updated smoking status.\n")
table(AEDB$SmokerStatus,
      useNA = "ifany", 
      dnn = c("Updated smoking status"))

cat("\n* Comparing to 'SmokerCurrent'.\n")
table(AEDB$SmokerStatus, AEDB$SmokerCurrent, 
      useNA = "ifany", 
      dnn = c("Updated smoking status", "Current smoker"))

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)


```

We will also fix the _alcohol_ status variable.


```{r FixAlcohol, message=FALSE, warning=FALSE}

# Fix diabetes
attach(AEDB)
AEDB[,"AlcoholUse"] <- NA
AEDB$AlcoholUse[diet810 == -999] <- NA
AEDB$AlcoholUse[diet810 == 0] <- "No"
AEDB$AlcoholUse[diet810 == 1] <- "Yes"
detach(AEDB)

table(AEDB$AlcoholUse)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)


```

We will also fix a history of CAD, stroke or peripheral intervention status variable. This will be based on `CAD_history`, `Stroke_history`, and `Peripheral.interv`

```{r FixCAD_History, message=FALSE, warning=FALSE}

# Fix diabetes
attach(AEDB)
AEDB[,"MedHx_CVD"] <- NA
AEDB$MedHx_CVD[CAD_history == 0 | Stroke_history == 0 | Peripheral.interv == 0] <- "No"
AEDB$MedHx_CVD[CAD_history == 1 | Stroke_history == 1 | Peripheral.interv == 1] <- "yes"
detach(AEDB)

table(AEDB$CAD_history)
table(AEDB$Stroke_history)
table(AEDB$Peripheral.interv)
table(AEDB$MedHx_CVD)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)


```




# Athero-Express Biobank Study

## Baseline characteristics

We are interested in the following variables at baseline.

- Age (years)
- Female sex (N, %)
- Hypertension (N, %)
- SBP (mmHg)
- DBP (mmHg)
- Diabetes mellitus (N, %)
- Total cholesterol levels (mg/dL)
- LDL cholesterol levels (mg/dL)
- HDL cholesterol levels (mg/dL)
- Triglyceride levels (mg/dL)
- Use of statins (N, %)
- Use of antiplatelet drugs (N, %)
- BMI (kg/m²)
- Smoking status (N, %)
  - Never smokers
  - Ex-smokers
  - Current smokers
- History of CAD (N, %)
- History of PAD (N, %)
- Clinical manifestations
  - Asymptomatic
  - Amaurosis fugax
  - TIA
  - Stroke
- eGFR (mL/min/1.73 m²)
- MCP-1 plaque levels (pg/mL)
- MCP-1 serum levels (pg/mL)


```{r Baseline AEDB: creation, include = FALSE}
cat("====================================================================================================\n")
cat("SELECTION THE SHIZZLE\n")

### Artery levels
# AEdata$Artery_summary: 
#           value                                                                                   label
# NOT USE - 0 No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA
# USE - 1                                                                  carotid (left & right)
# USE - 2                                               femoral/iliac (left, right or both sides)
# NOT USE - 3                                               other carotid arteries (common, external)
# NOT USE - 4                                   carotid bypass and injury (left, right or both sides)
# NOT USE - 5                                                         aneurysmata (carotid & femoral)
# NOT USE - 6                                                                                   aorta
# NOT USE - 7                                            other arteries (renal, popliteal, vertebral)
# NOT USE - 8                        femoral bypass, angioseal and injury (left, right or both sides)

### AEdata$informedconsent
#           value                                                                                           label
# NOT USE - -999                                                                                         missing
# NOT USE - 0                                                                                        no, died
# USE - 1                                                                                             yes
# USE - 2                                                             yes, health treatment when possible
# USE - 3                                                                        yes, no health treatment
# USE - 4                                                yes, no health treatment, no commercial business
# NOT USE - 5                                                          yes, no tissue, no commerical business
# NOT USE - 6                      yes, no tissue, no questionnaires, no medical info, no commercial business
# USE - 7                             yes, no questionnaires, no health treatment, no commercial business
# USE - 8                                          yes, no questionnaires, health treatment when possible
# NOT USE - 9                  yes, no tissue, no questionnaires, no health treatment, no commerical business
# USE - 10                               yes, no health treatment, no medical info, no commercial business
# NOT USE - 11 yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business
# USE - 12                                                     yes, no questionnaires, no health treatment
# NOT USE - 13                                                             yes, no tissue, no health treatment
# NOT USE - 14                                                               yes, no tissue, no questionnaires
# NOT USE - 15                                                  yes, no tissue, health treatment when possible
# NOT USE - 16                                                                                  yes, no tissue
# USE - 17                                                                     yes, no commerical business
# USE - 18                                     yes, health treatment when possible, no commercial business
# USE - 19                                                    yes, no medical info, no commercial business
# USE - 20                                                                          yes, no questionnaires
# NOT USE - 21                         yes, no tissue, no questionnaires, no health treatment, no medical info
# NOT USE - 22                  yes, no tissue, no questionnaires, no health treatment, no commercial business
# USE - 23                                                                            yes, no medical info
# USE - 24                                                  yes, no questionnaires, no commercial business
# USE - 25                                    yes, no questionnaires, no health treatment, no medical info
# USE - 26                  yes, no questionnaires, health treatment when possible, no commercial business
# USE - 27                                                      yes,  no health treatment, no medical info
# NOT USE - 28                                                                             no, doesn't want to
# NOT USE - 29                                                                              no, unable to sign
# NOT USE - 30                                                                                 no, no reaction
# NOT USE - 31                                                                                        no, lost
# NOT USE - 32                                                                                     no, too old
# NOT USE - 34                                            yes, no medical info, health treatment when possible
# NOT USE - 35                                             no (never asked for IC because there was no tissue)
# USE - 36                    yes, no medical info, no commercial business, health treatment when possible
# NOT USE - 37                                                                                    no, endpoint
# USE - 38                                                         wil niets invullen, wel alles gebruiken
# USE - 39                                           second informed concents: yes, no commercial business
# NOT USE - 40                                                                              nooit geincludeerd

cat("- sanity checking PRIOR to selection")
library(data.table)
ae.gender <- ifelse(AEDB$Gender == 0, "Female", "Male")
ae.hospital <- ifelse(AEDB$Hospital == 1, "Antonius", "UMCU")
table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"))
ae.gender <- ifelse(AEDB$Gender == 0, "Female", "Male")
table(ae.gender, AEDB$Artery_summary, dnn = c("Sex", "Artery"))
# table(ae.gender, AEDB$informedconsent, dnn = c("Sex", "IC"))

rm(ae.gender, ae.hospital)

# I change numeric and factors manually because, well, I wouldn't know how to fix it otherwise
# to have this 'tibble' work with 'tableone'... :-)

AEDB$Age <- as.numeric(AEDB$Age)
AEDB$diastoli <- as.numeric(AEDB$diastoli)
AEDB$systolic <- as.numeric(AEDB$systolic)

AEDB$TC_finalCU <- as.numeric(AEDB$TC_finalCU)
AEDB$LDL_finalCU <- as.numeric(AEDB$LDL_finalCU)
AEDB$HDL_finalCU <- as.numeric(AEDB$HDL_finalCU)
AEDB$TG_finalCU <- as.numeric(AEDB$TG_finalCU)

AEDB$TC_final <- as.numeric(AEDB$TC_final)
AEDB$LDL_final <- as.numeric(AEDB$LDL_final)
AEDB$HDL_final <- as.numeric(AEDB$HDL_final)
AEDB$TG_final <- as.numeric(AEDB$TG_final)

AEDB$Age <- as.numeric(AEDB$Age)
AEDB$GFR_MDRD <- as.numeric(AEDB$GFR_MDRD)
AEDB$BMI <- as.numeric(AEDB$BMI)
AEDB$eCigarettes <- as.numeric(AEDB$eCigarettes)
AEDB$ePackYearsSmoking <- as.numeric(AEDB$ePackYearsSmoking)
AEDB$EP_composite_time <- as.numeric(AEDB$EP_composite_time)

AEDB$macmean0 <- as.numeric(AEDB$macmean0)
AEDB$smcmean0 <- as.numeric(AEDB$smcmean0)
AEDB$neutrophils <- as.numeric(AEDB$neutrophils)
AEDB$Mast_cells_plaque <- as.numeric(AEDB$Mast_cells_plaque)
AEDB$vessel_density_averaged <- as.numeric(AEDB$vessel_density_averaged)

AEDB$IL6 <- as.numeric(AEDB$IL6)
AEDB$IL6_pg_ug_2015 <- as.numeric(AEDB$IL6_pg_ug_2015)
AEDB$IL6R_pg_ug_2015 <- as.numeric(AEDB$IL6R_pg_ug_2015)
AEDB$MCP1 <- as.numeric(AEDB$MCP1)
AEDB$MCP1_pg_ug_2015 <- as.numeric(AEDB$MCP1_pg_ug_2015)
AEDB$hsCRP_plasma <- as.numeric(AEDB$hsCRP_plasma)

require(labelled)
AEDB$ORyear <- to_factor(AEDB$ORyear)
AEDB$Gender <- to_factor(AEDB$Gender)
AEDB$Hospital <- to_factor(AEDB$Hospital)
AEDB$KDOQI <- to_factor(AEDB$KDOQI)
AEDB$BMI_WHO <- to_factor(AEDB$BMI_WHO)
AEDB$DiabetesStatus <- to_factor(AEDB$DiabetesStatus)
AEDB$SmokerStatus <- to_factor(AEDB$SmokerStatus)
AEDB$AlcoholUse <- to_factor(AEDB$AlcoholUse)

AEDB$Hypertension.selfreport <- to_factor(AEDB$Hypertension1)
AEDB$Hypertension.selfreportdrug <- to_factor(AEDB$Hypertension2)
AEDB$Hypertension.composite <- to_factor(AEDB$Hypertension.composite)
AEDB$Hypertension.drugs <- to_factor(AEDB$Hypertension.drugs)

AEDB$Med.anticoagulants <- to_factor(AEDB$Med.anticoagulants)
AEDB$Med.all.antiplatelet <- to_factor(AEDB$Med.all.antiplatelet)
AEDB$Med.Statin.LLD <- to_factor(AEDB$Med.Statin.LLD)

AEDB$Stroke_Dx <- to_factor(AEDB$Stroke_Dx)
AEDB$CAD_history <- to_factor(AEDB$CAD_history)
AEDB$PAOD <- to_factor(AEDB$PAOD)
AEDB$Peripheral.interv <- to_factor(AEDB$Peripheral.interv)
AEDB$MedHx_CVD <- to_factor(AEDB$MedHx_CVD)


AEDB$sympt <- to_factor(AEDB$sympt)
AEDB$Symptoms.3g <- to_factor(AEDB$Symptoms.3g)
AEDB$Symptoms.4g <- to_factor(AEDB$Symptoms.4g)
AEDB$Symptoms.5G <- to_factor(AEDB$Symptoms.5G)
AEDB$AsymptSympt <- to_factor(AEDB$AsymptSympt)
AEDB$AsymptSympt2G <- to_factor(AEDB$AsymptSympt2G)


AEDB$restenos <- to_factor(AEDB$restenos)
AEDB$stenose <- to_factor(AEDB$stenose)
AEDB$EP_composite <- to_factor(AEDB$EP_composite)
AEDB$Macrophages.bin <- to_factor(AEDB$Macrophages.bin)
AEDB$SMC.bin <- to_factor(AEDB$SMC.bin)
AEDB$IPH.bin <- to_factor(AEDB$IPH.bin)
AEDB$Calc.bin <- to_factor(AEDB$Calc.bin)
AEDB$Collagen.bin <- to_factor(AEDB$Collagen.bin)
AEDB$Fat.bin_10 <- to_factor(AEDB$Fat.bin_10)
AEDB$Fat.bin_40 <- to_factor(AEDB$Fat.bin_40)
AEDB$OverallPlaquePhenotype <- to_factor(AEDB$OverallPlaquePhenotype)

AEDB$Artery_summary <- to_factor(AEDB$Artery_summary)

AEDB$informedconsent <- to_factor(AEDB$informedconsent)

AEDB.CEA <- subset(AEDB,
                    (Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)") & # we only want carotids
                       informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
                       informedconsent != "no, died" &
                       informedconsent != "yes, no tissue, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no health treatment" &
                       informedconsent != "yes, no tissue, no questionnaires" &
                       informedconsent != "yes, no tissue, health treatment when possible" &
                       informedconsent != "yes, no tissue" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
                       informedconsent != "no, doesn't want to" &
                       informedconsent != "no, unable to sign" &
                       informedconsent != "no, no reaction" &
                       informedconsent != "no, lost" &
                       informedconsent != "no, too old" &
                       informedconsent != "yes, no medical info, health treatment when possible" &
                       informedconsent != "no (never asked for IC because there was no tissue)" &
                       informedconsent != "no, endpoint" &
                       informedconsent != "nooit geincludeerd" & 
                     !is.na(AsymptSympt2G))
# AEDB.CEA[1:10, 1:10]
dim(AEDB.CEA)
```

```{r}
cat("===========================================================================================\n")
cat("CREATE BASELINE TABLE\n")

# Baseline table variables
basetable_vars = c("Hospital", "ORyear",
                   "Age", "Gender", 
                   "TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU", 
                   "TC_final", "LDL_final", "HDL_final", "TG_final", 
                   "hsCRP_plasma",
                   "systolic", "diastoli", "GFR_MDRD", "BMI", 
                   "KDOQI", "BMI_WHO",
                   "SmokerStatus", "AlcoholUse",
                   "DiabetesStatus", 
                   "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                   "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                   "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                   "restenos", "stenose",
                   "MedHx_CVD", "CAD_history", "PAOD", "Peripheral.interv", 
                   "EP_composite", "EP_composite_time",
                   "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
                   "neutrophils", "Mast_cells_plaque",
                   "IPH.bin", "vessel_density_averaged",
                   "Calc.bin", "Collagen.bin", 
                   "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
                   "IL6", "IL6_pg_ug_2015", "IL6R_pg_ug_2015",
                   "MCP1", "MCP1_pg_ug_2015")

basetable_bin = c("Gender", 
                  "KDOQI", "BMI_WHO",
                  "SmokerStatus", "AlcoholUse",
                  "DiabetesStatus", 
                  "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                  "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                  "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                  "restenos", "stenose",
                  "CAD_history", "PAOD", "Peripheral.interv", 
                  "EP_composite", "Macrophages.bin", "SMC.bin",
                  "IPH.bin", 
                  "Calc.bin", "Collagen.bin", 
                  "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype")
# basetable_bin

basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_con
```

Showing the baseline table of the whole Athero-Express Biobank.

```{r Baseline AEDB: Visualize AEDB}
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "Symptoms.4g",
                                         data = AEDB, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:3]
```


```{r Baseline AEDB: Visualize AEDB CEA}
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.CEA.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "Symptoms.4g",
                                         data = AEDB.CEA, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:3]
```

```{r Baseline AEDB: Visualize subsetCEA}
AEDB.CEA.subset <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015))

AEDB.CEA.subset.AsymptSympt.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:6]
```

```{r Baseline AEDB: Visualize subsetCEA with MCP1}

AEDB.CEA.subset.combo <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015) | !is.na(MCP1))

AEDB.CEA.subset.combo.tableOne = print(CreateTableOne(vars = basetable_vars,
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset.combo, includeNA = TRUE),
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
                          format = "pf",
                          contDigits = 3)[,1:6]
```

```{r Baseline AEDB: Visualize subsetCEA with serum MCP1}
AEDB.CEA.subset.serum <- subset(AEDB.CEA, !is.na(MCP1))

AEDB.CEA.subset.serum.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset.serum, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:6]
```

```{r Baseline AEDB: Visualize subsetCEA with serum MCP1 and plaque MCP1}
AEDB.CEA.subset.both <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015) & !is.na(MCP1))

AEDB.CEA.subset.both.tableOne = print(CreateTableOne(vars = basetable_vars,
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset.both, includeNA = TRUE),
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
                          format = "pf",
                          contDigits = 3)[,1:6]
```

Writing the baseline table to Excel format. 
```{r Baseline AEDB: write}
# Write basetable
require(openxlsx)

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.xlsx"),
           AEDB.CEA.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "wholeAEDB_Baseline")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.AsymptSympt.xlsx"),
           AEDB.CEA.subset.AsymptSympt.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "wholeAEDB_Baseline_Sympt")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEA.xlsx"),
           AEDB.CEA.subset.combo.tableOne,
           row.names = TRUE,
           col.names = TRUE,
           sheetName = "subsetAEDB_Baseline")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEAserum.AsymptSympt.xlsx"),
           AEDB.CEA.subset.serum.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "subsetAEDB_Baseline_serum_Sympt")

```


## Data exploration

Here we inspect the data and when necessary transform quantitative measures. We will inspect the raw, natural log transformed + the smallest measurement, and inverse-normal transformation. 


```{r DataExploration: MCP1 plaque}

summary(AEDB.CEA$MCP1_pg_ug_2015)

do.call(rbind , by(AEDB.CEA$MCP1_pg_ug_2015, AEDB.CEA$AsymptSympt2G, summary))

```

```{r DataExploration: MCP1 plaque visual}
library(patchwork)
p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 plaque levels",
                    xlab = "pg/ug", 
                    ggtheme = theme_minimal())

min_MCP1_pg_ug_2015 <- min(AEDB.CEA$MCP1_pg_ug_2015, na.rm = TRUE)
min_MCP1_pg_ug_2015

AEDB.CEA$MCP1_pg_ug_2015_LN <- log(AEDB.CEA$MCP1_pg_ug_2015 + min_MCP1_pg_ug_2015)
p2 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    # title = "MCP1 plaque levels",
                    xlab = "natural log-transformed pg/ug", 
                    ggtheme = theme_minimal())

AEDB.CEA$MCP1_pg_ug_2015_rank <- qnorm((rank(AEDB.CEA$MCP1_pg_ug_2015, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1_pg_ug_2015)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015_rank",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = "MCP1 plaque levels",
                    xlab = "inverse-normal transformation pg/ug",
                    ggtheme = theme_minimal())

p1 
p2 
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "")  | ggpar(p3, legend = "right")

rm(p1, p2, p3)
```


```{r DataExploration: MCP1 serum}

summary(AEDB.CEA$MCP1)

do.call(rbind , by(AEDB.CEA$MCP1, AEDB.CEA$AsymptSympt2G, summary))
do.call(rbind , by(AEDB.CEA.subset.serum$MCP1_pg_ug_2015, AEDB.CEA.subset.serum$AsymptSympt2G, summary))
do.call(rbind , by(AEDB.CEA.subset.serum$MCP1, AEDB.CEA.subset.serum$AsymptSympt2G, summary))

```

```{r DataExploration: MCP1 serum visual}
p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 serum levels",
                    xlab = "pg/mL", 
                    ggtheme = theme_minimal())

min_MCP1 <- min(AEDB.CEA$MCP1, na.rm = TRUE)
min_MCP1

AEDB.CEA$MCP1_LN <- log(AEDB.CEA$MCP1 + min_MCP1)
p2 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 serum levels",
                    xlab = "natural log-transformed pg/ug", 
                    ggtheme = theme_minimal())

AEDB.CEA$MCP1_rank <- qnorm((rank(AEDB.CEA$MCP1, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_rank",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = "MCP1 serum levels",
                    xlab = "inverse-normal transformation pg/ug",
                    ggtheme = theme_minimal())

p1 
p2 
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "")  | ggpar(p3, legend = "right")

rm(p1, p2, p3)

```

## Preliminary conclusion data exploration

In line with the previous work by [Marios Georgakis](https://www.ahajournals.org/doi/full/10.1161/CIRCRESAHA.119.315380){target="_blank"} we will apply _natural log transformation_ on all proteins and focus the analysis on MCP1 in serum and plaque.

# Analyses

The analyses are focused on three elements: 

1) plaque vulnerability phenotypes
2) clinical status at inclusion (symptoms)
3) secondary clinical outcome during three (3) years of follow-up

## Covariates & other variables

1.  Age (continuous in 1-year increment). [`Age`]
2.  Sex (male vs. female). [`Gender`]
3.  Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [`Hypertension.composite`]
4.  Presence of diabetes mellitus at baseline (defined either as a history of diabetes and/or administration of glucose lowering medication). [`DiabetesStatus`]
5.  Smoking (current, ex-, never). [`SmokerStatus`]
6.  LDL-C levels (continuous). [`LDL_final`]
7.  Use of lipid-lowering drugs. [`Med.Statin.LLD`]
8.  Use of antiplatelet drugs. [`Med.all.antiplatelet`]
9.  eGFR (continuous). [`GFR_MDRD`]
10.	BMI (continuous). [`BMI`]
11.	History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [`MedHx_CVD`] combination of [`CAD_history`, `Stroke_history`, `Peripheral.interv`]
12.	Level of stenosis (50-70% vs. 70-99%). [`stenose`]

## Models

We will analyze the data through four different models

- Model 1: adjusted for age and sex
- Model 2: adjusted for age, sex, and additionally adjusted for history hypertension (defined from medical history and/or use of antihypertensive medications), diabetes (defined as history of a diagnosis and/or use of glucose-lowering medications), current smoking, LDL-C levels at time of operation, use of statins, use of antiplatelet agents, eGFR, BMI, history of cardiovascular disease (coronary artery disease, stroke, peripheral artery disease), and level of stenosis (50-70%, 70-90%, 90-99%)

## A. Cross-sectional analysis plaque phenotypes

In the cross-sectional analysis of plaque and serum MCP1, IL6, and IL6R levels we will focus on the following plaque vulnerability phenotypes:

- Percentage of macrophages (continuous trait)
- Percentage of SMCs (continuous trait)
- Number of intraplaque microvessels per 3-4 hotspots (continuous trait)
- Presence of moderate/heavy calcifications (binary trait)
- Presence of moderate/heavy collagen content (binary trait)
- Presence of lipid core no/<10% vs. >10% (binary trait)
- Presence of intraplaque hemorrhage (binary trait)

*Continous traits*
```{r CrossSec: plaques - transformations and visualisations continuous}

# macrophages
cat("Summary of data.\n")
summary(AEDB.CEA$macmean0)

min_macmean <- min(AEDB.CEA$macmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % macrophages: ",min_macmean,".\n"))

AEDB.CEA$Macrophages_LN <- log(AEDB.CEA$macmean0 + min_macmean)

ggpubr::gghistogram(AEDB.CEA, "Macrophages_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "natural log-transformed %", 
                    ggtheme = theme_minimal())

AEDB.CEA$Macrophages_rank <- qnorm((rank(AEDB.CEA$macmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$macmean0)))
ggpubr::gghistogram(AEDB.CEA, "Macrophages_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())

# smooth muscle cells
cat("Summary of data.\n")
summary(AEDB.CEA$macmean0)

min_smcmean <- min(AEDB.CEA$smcmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % smooth muscle cells: ",min_smcmean,".\n"))

AEDB.CEA$SMC_LN <- log(AEDB.CEA$smcmean0 + min_smcmean)

ggpubr::gghistogram(AEDB.CEA, "SMC_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "natural log-transformed %", 
                    ggtheme = theme_minimal())

AEDB.CEA$SMC_rank <- qnorm((rank(AEDB.CEA$smcmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$smcmean0)))
ggpubr::gghistogram(AEDB.CEA, "SMC_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())

# vessel density
cat("Summary of data.\n")
summary(AEDB.CEA$vessel_density_averaged)

min_vesseldensity <- min(AEDB.CEA$vessel_density_averaged, na.rm = TRUE)
min_vesseldensity
cat(paste0("\nMinimum value number of intraplaque neovessels per 3-4 hotspots: ",min_vesseldensity,".\n"))

AEDB.CEA$VesselDensity_LN <- log(AEDB.CEA$vessel_density_averaged + min_vesseldensity)

ggpubr::gghistogram(AEDB.CEA, "VesselDensity_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                    xlab = "natural log-transformed number", 
                    ggtheme = theme_minimal())

AEDB.CEA$VesselDensity_rank <- qnorm((rank(AEDB.CEA$vessel_density_averaged, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$vessel_density_averaged)))
ggpubr::gghistogram(AEDB.CEA, "VesselDensity_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                   xlab = "inverse-rank normalized number", 
                    ggtheme = theme_minimal())
```

*Binary traits*
```{r CrossSec: plaques - transformations and visualisations binary}

# calcification
cat("Summary of data.\n")
summary(AEDB.CEA$Calc.bin)
contrasts(AEDB.CEA$Calc.bin)

AEDB.CEA$CalcificationPlaque <- as.factor(AEDB.CEA$Calc.bin)

df <- AEDB.CEA %>%
  filter(!is.na(CalcificationPlaque)) %>%
  group_by(Gender, CalcificationPlaque) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "CalcificationPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Calcification",
                    xlab = "calcification", 
                    ggtheme = theme_minimal())
rm(df)

# collagen
cat("Summary of data.\n")
summary(AEDB.CEA$Collagen.bin)
contrasts(AEDB.CEA$Collagen.bin)

AEDB.CEA$CollagenPlaque <- as.factor(AEDB.CEA$Collagen.bin)

df <- AEDB.CEA %>%
  filter(!is.na(CollagenPlaque)) %>%
  group_by(Gender, CollagenPlaque) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "CollagenPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Collagen",
                    xlab = "collagen", 
                    ggtheme = theme_minimal())
rm(df)

# fat 10%
cat("Summary of data.\n")
summary(AEDB.CEA$Fat.bin_10)
contrasts(AEDB.CEA$Fat.bin_10)

AEDB.CEA$Fat10Perc <- as.factor(AEDB.CEA$Fat.bin_10)

df <- AEDB.CEA %>%
  filter(!is.na(Fat10Perc)) %>%
  group_by(Gender, Fat10Perc) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "Fat10Perc", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque fat",
                    xlab = "intraplaque fat", 
                    ggtheme = theme_minimal())
rm(df)

# IPH
cat("Summary of data.\n")
summary(AEDB.CEA$IPH.bin)
contrasts(AEDB.CEA$IPH.bin)

AEDB.CEA$IPH <- as.factor(AEDB.CEA$IPH.bin)

df <- AEDB.CEA %>%
  filter(!is.na(IPH)) %>%
  group_by(Gender, IPH) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "IPH", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque hemorrhage",
                    xlab = "intraplaque hemorrhage", 
                    ggtheme = theme_minimal())
rm(df)

# Symptoms
cat("Summary of data.\n")
summary(AEDB.CEA$AsymptSympt)
contrasts(AEDB.CEA$AsymptSympt)

AEDB.CEA$AsymptSympt <- as.factor(AEDB.CEA$AsymptSympt)

df <- AEDB.CEA %>%
  filter(!is.na(AsymptSympt)) %>%
  group_by(Gender, AsymptSympt) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "AsymptSympt", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Symptoms",
                    xlab = "symptoms", 
                    ggtheme = theme_minimal())
rm(df)

```

In this section we make some variables to assist with analysis.
```{r CrossSec: plaques - setup regression }
AEDB.CEA.samplesize = nrow(AEDB.CEA)
# TRAITS.PROTEIN = c("IL6_LN", "MCP1_LN", "IL6_pg_ug_2015_LN", "IL6R_pg_ug_2015_LN", "MCP1_pg_ug_2015_LN")
# TRAITS.PROTEIN.RANK = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank")
TRAITS.PROTEIN.RANK = c("MCP1_pg_ug_2015_rank", "MCP1_rank")

# TRAITS.CON = c("Macrophages_LN", "SMC_LN", "VesselDensity_LN") 
TRAITS.CON.RANK = c("Macrophages_rank", "SMC_rank", "VesselDensity_rank")

TRAITS.BIN = c("CalcificationPlaque", "CollagenPlaque", "Fat10Perc", "IPH")

# "Hospital", 
# "Age", "Gender", 
# "TC_final", "LDL_final", "HDL_final", "TG_final", 
# "systolic", "diastoli", "GFR_MDRD", "BMI", 
# "KDOQI", "BMI_WHO",
# "SmokerCurrent", "eCigarettes", "ePackYearsSmoking",
# "DiabetesStatus", "Hypertension.composite", 
# "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
# "Stroke_Dx", "sympt", "Symptoms.5G", "restenos",
# "EP_composite", "EP_composite_time",
# "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
# "neutrophils", "Mast_cells_plaque",
# "IPH.bin", "vessel_density_averaged",
# "Calc.bin", "Collagen.bin", 
# "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
# "IL6_pg_ug_2015", "MCP1_pg_ug_2015", 
# "QC2018_FILTER", "CHIP", "SAMPLE_TYPE",
# "CAD_history", "Stroke_history", "Peripheral.interv",
# "stenose"

# 1.  Age (continuous in 1-year increment). [Age]
# 2.  Sex (male vs. female). [Gender]
# 3.  Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
# 4.  Presence of diabetes mellitus at baseline (defined either as a history of diabetes, administration of glucose lowering medication, HbA1c ≥6.5%, fasting glucose ≥126 mg/dl, .or random glucose levels ≥200 mg/dl). [DiabetesStatus]
# 5.  Smoking (current, ex-, never). [SmokerCurrent]
# 6.  LDL-C levels (continuous). [LDL_final]
# 7.  Use of lipid-lowering drugs. [Med.Statin.LLD]
# 8.  Use of antiplatelet drugs. [Med.all.antiplatelet]
# 9.  eGFR (continuous). [GFR_MDRD]
# 10.	BMI (continuous). [BMI]
# 11.	History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [MedHx_CVD] combinatino of: [CAD_history, Stroke_history, Peripheral.interv]
# 12.	Level of stenosis (50-70% vs. 70-99%). [stenose]

# Models 
# Model 1: adjusted for age and sex
# Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,

COVARIATES_M1 = c("Age", "Gender")

COVARIATES_M2 = c(COVARIATES_M1,  
               "Hypertension.composite", "DiabetesStatus", 
               "SmokerStatus", 
               # "SmokerCurrent",
               "Med.Statin.LLD", "Med.all.antiplatelet", 
               "GFR_MDRD", "BMI", 
               # "CAD_history", "Stroke_history", "Peripheral.interv", 
               "MedHx_CVD",
               "stenose")

# COVARIATES_M3 = c(COVARIATES_M2, "LDL_final")

# COVARIATES_M4 = c(COVARIATES_M2, "hsCRP_plasma")

# COVARIATES_M5 = c(COVARIATES_M2, "IL6_pg_ug_2015_LN")
# COVARIATES_M5rank = c(COVARIATES_M2, "IL6_pg_ug_2015_rank")

```

### Model 1

In this model we correct for _Age_ and _Gender_.

#### Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of continuous/quantitative plaque traits as a function of serum/plaque MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL1 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))

    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

Analysis of binary plaque traits as a function of serum/plaque MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL1 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender,
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


### Model 2

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis_.


#### Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of continuous/quantitative plaque traits as a function of serum/plaque MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

Analysis of binary plaque traits as a function of serum/plaque MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```



## B. Cross-sectional analysis symptoms

We will perform a cross-sectional analysis between plaque and serum MCP1, IL6, and IL6R levels and the 'clinical status' of the plaque in terms of presence of patients' symptoms (symptomatic vs. asymptomatic). The symptoms of interest are:

- stroke
- TIA
- retinal infarction
- amaurosis fugax
- asymptomatic

### Model 1

In this model we correct for _Age_, and _Gender_.


#### Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.

```{r CrossSec: symptoms - logistic regression MODEL1 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     # + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
     #            Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
     #            CAD_history + Stroke_history + Peripheral.interv + stenose
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

### Model 2

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis._.


#### Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.

```{r CrossSec: symptoms - logistic regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
                MedHx_CVD + + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


## C. Longitudinal analysis secondary clinical outcome

For the longitudinal analyses of plaque and serum MCP1, IL6, and IL6R levels and secondary cardiovascular events over a three-year follow-up period. 

The _primary outcome_ is defined as "a composite of fatal or non-fatal myocardial infarction, fatal or non-fatal stroke, ruptured aortic aneurysm, fatal cardiac failure, coronary or peripheral interventions, leg amputation due to vascular causes, and cardiovascular death", i.e. major adverse cardiovascular events (MACE). Variable: `epmajor.3years`, these include:
- myocardial infarction (MI)
- cerebral infarction (CVA/stroke)
- cardiovascular death (exact cause to be investigated)
- cerebral bleeding (CVA/stroke)
- fatal myocardial infarction (MI)
- fatal cerebral infarction
- fatal cerebral bleeding
- sudden death
- fatal heart failure
- fatal aneurysm rupture
- other cardiovascular death..

The _secondary outcomes_ will be 

- incidence of fatal or non-fatal stroke (ischemic and bleeding) - variable: `epstroke.3years`, these include:
  - cerebral infarction (CVA/stroke)
  - cerebral bleeding (CVA/stroke)
  - fatal cerebral infarction
  - fatal cerebral bleeding.
- incidence of acute coronary events (fatal or non-fatal myocardial infarction, coronary interventions) - variable: `epcoronary.3years`, these include:
  - myocardial infarction (MI)
  - coronary angioplasty (PCI/PTCA)
  - cardiovascular death (exact cause to be investigated)
  - coronary bypass (CABG)
  - fatal myocardial infarction (MI)
  - sudden death.
- cardiovascular death - variable: `epcvdeath.3years`, these include:
  - cardiovascular death (exact cause to be investigated)
  - fatal myocardial infarction (MI)
  - fatal cerebral infarction
  - fatal cerebral bleeding
  - sudden death
  - fatal heart failure
  - fatal aneurysm rupture
  - other cardiovascular death..

### 30- and 90-days FU events

We will use 3-year follow-up, but we will also calculate 30 days and 90 days follow-up 'time-to-event' variables. On average there are 365.25 days in a year. We can calculate 30-days and 90-days follow-up time based on the three years follow-up. 

```{r Calculate new FU cut-offs: maximum FU}
cutt.off.30days = (1/365.25) * 30
cutt.off.90days = (1/365.25) * 90

# Fix maximum FU of 30 and 90 days
AEDB <- AEDB %>%
  mutate(
    FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
    FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
  ) 

AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "max.followup", 
                                      "FU.cutt.off.3years",
                                      "FU.cutt.off.30days", 
                                      "FU.cutt.off.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)

rm(AEDB.temp)

AEDB.CEA <- AEDB.CEA %>%
  mutate(
    FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
    FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
  ) 

AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "max.followup", 
                                      "FU.cutt.off.3years",
                                      "FU.cutt.off.30days", 
                                      "FU.cutt.off.90days"))
require(labelled)
AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)

DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)

rm(AEDB.CEA.temp)


```

Here we will calculate the new 30- and 90-days follow-up of the events and their event-times of interest:

- MACE (`epmajor.3years`)
- Stroke (`epstroke.3years`)
- Coronary events (`epcoronary.3years`)
- Cardiovascular death (`epcvdeath.3years`)


```{r Calculate new FU cut-offs: times}
avg_days_in_year = 365.25
cutt.off.30days.scaled <- cutt.off.30days * 365.25
cutt.off.90days.scaled <- cutt.off.90days * 365.25
# Event times
AEDB <- AEDB %>%
  mutate(
    ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
  ) 

AEDB.CEA <- AEDB.CEA %>%
  mutate(
    ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
  ) 

```


```{r Cox-regressions: new times}

attach(AEDB)
AEDB[,"epmajor.30days"] <- AEDB$epmajor.3years
AEDB$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0

AEDB[,"epstroke.30days"] <- AEDB$epstroke.3years
AEDB$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0

AEDB[,"epcoronary.30days"] <- AEDB$epcoronary.3years
AEDB$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0

AEDB[,"epcvdeath.30days"] <- AEDB$epcvdeath.3years
AEDB$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0

AEDB[,"epmajor.90days"] <- AEDB$epmajor.3years
AEDB$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0

AEDB[,"epstroke.90days"] <- AEDB$epstroke.3years
AEDB$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0

AEDB[,"epcoronary.90days"] <- AEDB$epcoronary.3years
AEDB$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0

AEDB[,"epcvdeath.90days"] <- AEDB$epcvdeath.3years
AEDB$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0

detach(AEDB)

AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
                                      "epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
                                      "epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)

rm(AEDB.temp)

attach(AEDB.CEA)
AEDB.CEA[,"epmajor.30days"] <- AEDB.CEA$epmajor.3years
AEDB.CEA$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epstroke.30days"] <- AEDB.CEA$epstroke.3years
AEDB.CEA$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epcoronary.30days"] <- AEDB.CEA$epcoronary.3years
AEDB.CEA$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epcvdeath.30days"] <- AEDB.CEA$epcvdeath.3years
AEDB.CEA$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epmajor.90days"] <- AEDB.CEA$epmajor.3years
AEDB.CEA$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0

AEDB.CEA[,"epstroke.90days"] <- AEDB.CEA$epstroke.3years
AEDB.CEA$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0

AEDB.CEA[,"epcoronary.90days"] <- AEDB.CEA$epcoronary.3years
AEDB.CEA$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0

AEDB.CEA[,"epcvdeath.90days"] <- AEDB.CEA$epcvdeath.3years
AEDB.CEA$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0

detach(AEDB.CEA)

AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
                                      "epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
                                      "epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)

DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)

rm(AEDB.CEA.temp)



```

### Sanity checks

First we do some sanity checks and inventory the time-to-event and event variables.
```{r Cox-regressions: General}
# Reference: https://bioconductor.org/packages/devel/bioc/vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html
# If you want to suppress warnings and messages when installing/loading packages
# suppressPackageStartupMessages({})
install.packages.auto("survival")
install.packages.auto("survminer")
install.packages.auto("Hmisc")

cat("* Creating function to summarize Cox regression and prepare container for results.")
# Function to get summary statistics from Cox regression model
COX.STAT <- function(coxfit, DATASET, OUTCOME, protein){
  cat("Summarizing Cox regression results for '", protein ,"' and its association to '",OUTCOME,"' in '",DATASET,"'.\n")
  if (nrow(summary(coxfit)$coefficients) == 1) {
    output = c(protein, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    cox.sum <- summary(coxfit)
    cox.effectsize = cox.sum$coefficients[1,1]
    cox.SE = cox.sum$coefficients[1,3]
    cox.HReffect = cox.sum$coefficients[1,2]
    cox.CI_low = exp(cox.effectsize - 1.96 * cox.SE)
    cox.CI_up = exp(cox.effectsize + 1.96 * cox.SE)
    cox.zvalue = cox.sum$coefficients[1,4]
    cox.pvalue = cox.sum$coefficients[1,5]
    cox.sample_size = cox.sum$n
    cox.nevents = cox.sum$nevent
    
    output = c(DATASET, OUTCOME, protein, cox.effectsize, cox.SE, cox.HReffect, cox.CI_low, cox.CI_up, cox.zvalue, cox.pvalue, cox.sample_size, cox.nevents)
    cat("We have collected the following:\n")
    cat("Dataset used..............:", DATASET, "\n")
    cat("Outcome analyzed..........:", OUTCOME, "\n")
    cat("Protein...................:", protein, "\n")
    cat("Effect size...............:", round(cox.effectsize, 6), "\n")
    cat("Standard error............:", round(cox.SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(cox.HReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(cox.CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(cox.CI_up, 3), "\n")
    cat("T-value...................:", round(cox.zvalue, 6), "\n")
    cat("P-value...................:", signif(cox.pvalue, 8), "\n")
    cat("Sample size in model......:", cox.sample_size, "\n")
    cat("Number of events..........:", cox.nevents, "\n")
  }
  return(output)
  print(output)
} 

times = c("ep_major_t_3years", 
          "ep_stroke_t_3years", "ep_coronary_t_3years", "ep_cvdeath_t_3years")

endpoints = c("epmajor.3years", 
              "epstroke.3years", "epcoronary.3years", "epcvdeath.3years")

cat("* Check the cases per event type - for sanity.")
for (events in endpoints){
  require(labelled)
  print(paste0("Printing the summary of: ",events))
  # print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}

cat("* Check distribution of events over time - for sanity.")
for (eventtimes in times){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}

for (eventtime in times){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "year", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.EventDistributionPerYear.",eventtime,".pdf"), plot = last_plot())
}

times30 = c("ep_major_t_30days", 
          "ep_stroke_t_30days", "ep_coronary_t_30days", "ep_cvdeath_t_30days")

endpoints30 = c("epmajor.30days", 
              "epstroke.30days", "epcoronary.30days", "epcvdeath.30days")

cat("* Check the cases per event type - for sanity.")
for (events in endpoints30){
  print(paste0("Printing the summary of: ",events))
  # print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}

cat("* Check distribution of events over time - for sanity.")
for (eventtimes in times30){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}

for (eventtime in times30){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.EventDistributionPer30Days.",eventtime,".pdf"), plot = last_plot())
}

times90 = c("ep_major_t_90days", 
          "ep_stroke_t_90days", "ep_coronary_t_90days", "ep_cvdeath_t_90days")

endpoints90 = c("epmajor.90days", 
              "epstroke.90days", "epcoronary.90days", "epcvdeath.90days")

cat("* Check the cases per event type - for sanity.")
for (events in endpoints90){
  print(paste0("Printing the summary of: ",events))
  # print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}

cat("* Check distribution of events over time - for sanity.")
for (eventtimes in times90){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}

for (eventtime in times90){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.EventDistributionPer90Days.",eventtime,".pdf"), plot = last_plot())
}


```


### Cox regressions

Let's perform the actual Cox-regressions. We will apply a couple of models: 

- Model 1: adjusted for age and sex
- Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis

#### 3 years follow-up

*MODEL 1*
```{r Cox-regression Analysis: Simple model}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)

```

*MODEL 2*
```{r Cox-regression Analysis: MODEL 2}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)

```


#### 30-days follow-up

*MODEL 1*
```{r Cox-regression Analysis: Simple model, 30 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times30)){
  eptime = times30[i]
  ep = endpoints30[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".30days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.30days.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.30days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)

```

*MODEL 2*
```{r Cox-regression Analysis: MODEL 2, 30 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times30)){
  eptime = times30[i]
  ep = endpoints30[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".30days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.30days.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.30days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)

```


#### 90-days follow-up

*MODEL 1*
```{r Cox-regression Analysis: Simple model, 90 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times90)){
  eptime = times90[i]
  ep = endpoints90[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".90days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.90days.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.90days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)

```

*MODEL 2*
```{r Cox-regression Analysis: MODEL 2, 90 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times90)){
  eptime = times90[i]
  ep = endpoints90[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".90days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.90days.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.90days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)

```



# Correlations

## All biomarkers
We correlated serum and plaque levels of the biomarkers.

```{r CrossSampleType Correlations}

# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)


# Creating matrix - inverse-rank transformation
# --------------------------------
# AEDB.CEA.temp <- subset(AEDB.CEA, 
#                           select = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank",
#                                                TRAITS.BIN, TRAITS.CON.RANK)
#                                     )
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c("MCP1_rank", "MCP1_pg_ug_2015_rank",
                                               TRAITS.BIN, TRAITS.CON.RANK)
                                    )

AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
str(AEDB.CEA.temp)
AEDB.CEA.matrix.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers.rank <- round(cor(AEDB.CEA.matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.RANK, use = "pairwise.complete.obs", method = "spearman")

# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))


# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    biomarker_row = rownames(cormat)[row(cormat)[ut]],
    biomarker_column = rownames(cormat)[col(cormat)[ut]],
    spearman_cor  =(cormat)[ut],
    pval = pmat[ut]
    )
}

corr_biomarkers.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank))
DT::datatable(corr_biomarkers.rank.df)

# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall", 
    "spearman"), ...) 
{
    x = checkData(R, method = "matrix")
    if (missing(method)) 
        method = method[1]
    cormeth <- method
    panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs", 
        method = cormeth, cex.cor, ...) {
        usr <- par("usr")
        on.exit(par(usr))
        par(usr = c(0, 1, 0, 1))
        r <- cor(x, y, use = use, method = method)
        txt <- format(c(r, 0.123456789), digits = digits)[1]
        txt <- paste(prefix, txt, sep = "")
        if (missing(cex.cor)) 
            cex <- 0.8/strwidth(txt)
        test <- cor.test(as.numeric(x), as.numeric(y), method = method)
        Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, 
            cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", 
                "**", "*", ".", " "))
        text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
        text(0.8, 0.8, Signif, cex = cex, col = 2)
    }
    f <- function(t) {
        dnorm(t, mean = mean(x), sd = sd.xts(x))
    }
    dotargs <- list(...)
    dotargs$method <- NULL
    rm(method)
    hist.panel = function(x, ... = NULL) {
        par(new = TRUE)
        hist(x, col = "#1290D9", probability = TRUE, axes = FALSE, 
        # hist(x, col = "light gray", probability = TRUE, axes = FALSE, 
            main = "", breaks = "FD")
        lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
        rug(x)
    }
    if (histogram) 
        pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, 
            diag.panel = hist.panel, ...)
    else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}


chart.Correlation.new(AEDB.CEA.matrix.RANK, method = "spearman", histogram = TRUE, pch = 3)


# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")

# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables 
library(GGally)
 
# From the help page:
ggpairs(AEDB.CEA, 
        columns = c("MCP1_rank", "MCP1_pg_ug_2015_rank", TRAITS.BIN, TRAITS.CON.RANK), 
        columnLabels = c("MCP1 (serum)", "MCP1", 
                         "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density"),
        method = c("spearman"),
        # ggplot2::aes(colour = Gender),
        progress = FALSE) 
```

## Circulating MCP1

Finally, we explored in a sub-sample, where circulating MCP-1 levels are available, the following:

1. A correlation between MCP-1 levels in the plaque and circulating MCP-1 levels
2. Associations of circulating MCP-1 levels with plaque vulnerability characteristics
3. Associations of circulating MCP-1 levels with the status of the plaque in terms of presence of symptoms (symptomatic vs. asymptomatic)
4. Associations of circulating MCP-1 levels with the primary composite endpoint of secondary cardiovascular events.

```{r MCP1 Serum Correlations}

# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)


# Creating matrix - inverse-rank transformation
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c("MCP1_rank", 
                                     TRAITS.BIN, TRAITS.CON.RANK, 
                                     "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite")
                                    )

AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
AEDB.CEA.temp$Symptoms.5G <- as.numeric(AEDB.CEA.temp$Symptoms.5G)
AEDB.CEA.temp$AsymptSympt <- as.numeric(AEDB.CEA.temp$AsymptSympt)
AEDB.CEA.temp$EP_major <- as.numeric(AEDB.CEA.temp$EP_major)
AEDB.CEA.temp$EP_composite <- as.numeric(AEDB.CEA.temp$EP_composite)
# str(AEDB.CEA.temp)
AEDB.CEA.matrix.serum.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers_serum.rank <- round(cor(AEDB.CEA.matrix.serum.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

corr_biomarkers_serum_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.serum.RANK, use = "pairwise.complete.obs", method = "spearman")

# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers_serum.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_serum_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))


# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    biomarker_row = rownames(cormat)[row(cormat)[ut]],
    biomarker_column = rownames(cormat)[col(cormat)[ut]],
    spearman_cor  =(cormat)[ut],
    pval = pmat[ut]
    )
}


corr_biomarkers_serum.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers_serum.rank, corr_biomarkers_serum_p.rank))
DT::datatable(corr_biomarkers_serum.rank.df)

# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall", 
    "spearman"), ...) 
{
    x = checkData(R, method = "matrix")
    if (missing(method)) 
        method = method[1]
    cormeth <- method
    panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs", 
        method = cormeth, cex.cor, ...) {
        usr <- par("usr")
        on.exit(par(usr))
        par(usr = c(0, 1, 0, 1))
        r <- cor(x, y, use = use, method = method)
        txt <- format(c(r, 0.123456789), digits = digits)[1]
        txt <- paste(prefix, txt, sep = "")
        if (missing(cex.cor)) 
            cex <- 0.8/strwidth(txt)
        test <- cor.test(as.numeric(x), as.numeric(y), method = method)
        Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, 
            cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", 
                "**", "*", ".", " "))
        text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
        text(0.8, 0.8, Signif, cex = cex, col = 2)
    }
    f <- function(t) {
        dnorm(t, mean = mean(x), sd = sd.xts(x))
    }
    dotargs <- list(...)
    dotargs$method <- NULL
    rm(method)
    hist.panel = function(x, ... = NULL) {
        par(new = TRUE)
        hist(x, col = "#1290D9", probability = TRUE, axes = FALSE, 
        # hist(x, col = "light gray", probability = TRUE, axes = FALSE, 
            main = "", breaks = "FD")
        lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
        rug(x)
    }
    if (histogram) 
        pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, 
            diag.panel = hist.panel, ...)
    else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}

chart.Correlation.new(AEDB.CEA.matrix.serum.RANK, method = "spearman", histogram = TRUE, pch = 3)


# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")

# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables 
library(GGally)
 
# From the help page:
ggpairs(AEDB.CEA,
        columns = c("MCP1_rank", TRAITS.BIN, TRAITS.CON.RANK, "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite"), 
        columnLabels = c("MCP1 (serum)", 
                         "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density",
                         "Symptoms", "Symptoms (grouped)", "MACE", "Composite"),
        method = c("spearman"),
        # ggplot2::aes(colour = Gender),
        progress = FALSE) 
```

# Additional figures

## Age and sex
We want to create per-age-group figures. 

- Box and Whisker plot for MCP-1 plaque levels by sex and age group (<55, 55-64, 65-74, 75-84, 85+)
- Box and Whisker plot for MCP-1 serum levels by sex and age group (<55, 55-64, 65-74, 75-84, 85+)
- Scatter plot of the correlation and regression line between MCP-1 levels in plaque (y axis) and serum (x axis).


```{r AgeGroups}
library(dplyr)

AEDB.CEA <- AEDB.CEA %>% mutate(AgeGroup = case_when(Age < 55 ~ "<55",
                                                     Age >= 55  & Age <= 64 ~ "55-64",
                                                     Age >= 65  & Age <= 74 ~ "65-74",
                                                     Age >= 75  & Age <= 84 ~ "75-84",
                                                     Age >= 85 ~ "85+"))
AEDB.CEA <- AEDB.CEA %>% mutate(AgeGroupSex = case_when(Age < 55 & Gender == "male" ~ "<55 males" ,
                                                        Age >= 55  & Age <= 64 & Gender == "male"~ "55-64 males",
                                                        Age >= 65  & Age <= 74 & Gender == "male"~ "65-74 males",
                                                        Age >= 75  & Age <= 84 & Gender == "male"~ "75-84 males",
                                                        Age >= 85 & Gender == "male"~ "85+ males",
                                                        Age < 55 & Gender == "female" ~ "<55 females" ,
                                                        Age >= 55  & Age <= 64 & Gender == "female"~ "55-64 females ",
                                                        Age >= 65  & Age <= 74 & Gender == "female"~ "65-74 females",
                                                        Age >= 75  & Age <= 84 & Gender == "female"~ "75-84 females",
                                                        Age >= 85 & Gender == "female"~ "85+ females"))

table(AEDB.CEA$AgeGroup, AEDB.CEA$Gender)
table(AEDB.CEA$AgeGroupSex)

```

Now we can draw some graphs of serum/plaque MCP1 levels per sex and age group.
```{r MCP1 per AgeGroup per Sex}

# ?ggpubr::ggboxplot()
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("Gender"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"))

ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"))

ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("Gender"),
                  y = "MCP1_rank", 
                  xlab = "Gender",
                  ylab = "MCP1 serum [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"))

ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1_rank", 
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 serum [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"))
```

Simalarly but now for the raw data as median ± interquartile range.
```{r MCP1 per AgeGroup per Sex, ranked}

# ?ggpubr::ggboxplot()
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("Gender"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "Gender",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "median_iqr")

ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "median_iqr")

ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("Gender"),
                  y = "MCP1", 
                  xlab = "Gender",
                  ylab = "MCP1 serum [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "median_iqr")

ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1", 
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 serum [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "median_iqr")
```

We will also make a nice correlation plot between serum and plaque MCP1 levels.
```{r MCP1 correlations}

ggpubr::ggscatter(AEDB.CEA, 
                  x = "MCP1_pg_ug_2015",
                  y = "MCP1", 
                  xlab = "MCP1 plaque [pg/ug]",
                  ylab = "MCP1 serum [pg/mL]",
                  add = "reg.line", add.params = list(color = "#1290D9"),
                  conf.int = TRUE,
                  cor.coef = TRUE, cor.coeff.args = list(method = "spearman"), cor.coef.coord = c(8,750))

ggpubr::ggscatter(AEDB.CEA, 
                  x = "MCP1_pg_ug_2015_rank",
                  y = "MCP1_rank", 
                  xlab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  ylab = "MCP1 serum [pg/mL]\n(inverse-rank transformation)",
                  add = "reg.line", add.params = list(color = "#1290D9"),
                  conf.int = TRUE,
                  cor.coef = TRUE, cor.coeff.args = list(method = "spearman"), cor.coef.coord = c(2,3))

```


## Symptoms
We want to create per-symptom figures. 


```{r SymptomGroups}
library(dplyr)

table(AEDB.CEA$AgeGroup, AEDB.CEA$AsymptSympt2G)
table(AEDB.CEA$Gender, AEDB.CEA$AsymptSympt2G)
table(AEDB.CEA$AsymptSympt2G)

```

Now we can draw some graphs of serum/plaque MCP1 levels per symptom group.
```{r MCP1 per SymptomGroups}

# ?ggpubr::ggboxplot()
my_comparisons <- list(c("Asymptomatic", "Symptomatic"))

p1 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "AsymptSympt2G", y = "MCP1_pg_ug_2015_rank",
                  title = "MCP1 plaque [pg/ug] levels per symptom", 
                  xlab = "Symptoms",
                  ylab = "MCP1 plaque [pg/ug]\n inverse-rank transformation",
                  color = "AsymptSympt2G", 
                  palette = c(uithof_color[16], uithof_color[23]),
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")

p1 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "AsymptSympt2G", y = "MCP1_rank",
                  title = "MCP1 serum [pg/mL] levels per symptom", 
                  xlab = "Symptoms",
                  ylab = "MCP1 serum [pg/mL]\n inverse-rank transformation",
                  color = "AsymptSympt2G", 
                  palette = c(uithof_color[16], uithof_color[23]),
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")
rm(p1)
```

We would also like to visualize the multivariable analyses results.
```{r load model data}
library(ggplot2)

model1_mcp1 <- read.xlsx(paste0(OUT_loc, "/", Today, ".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"))
model2_mcp1 <- read.xlsx(paste0(OUT_loc, "/", Today, ".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"))
model1_mcp1$model <- "univariate"
model2_mcp1$model <- "multivariate"

models_mcp1 <- rbind(model1_mcp1, model2_mcp1)
models_mcp1

```

```{r forestplot plaque}
dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"), 
                           
                           levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
                  cen = c(models_mcp1$OR[models_mcp1$Predictor=="MCP1_pg_ug_2015_rank"]),
                  low = c(models_mcp1$low95CI[models_mcp1$Predictor=="MCP1_pg_ug_2015_rank"]),
                  high = c(models_mcp1$up95CI[models_mcp1$Predictor=="MCP1_pg_ug_2015_rank"]))

fp <- ggplot(data=dat, aes(x=group, y=cen, ymin=low, ymax=high)) +
  geom_pointrange() + 
  geom_hline(yintercept=1, lty=2) +  # add a dotted line at x=1 after flip
  coord_flip() +  # flip coordinates (puts labels on y axis)
  xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
  theme(text = element_text(size=14)) +
  ggtitle("Plaque MCP-1 levels (1 SD increment)") +
  theme_minimal()  # use a white background
print(fp)
rm(fp)
```


```{r forestplot serum}
dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"), 
                           
                           levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
                  cen = c(models_mcp1$OR[models_mcp1$Predictor=="MCP1_rank"]),
                  low = c(models_mcp1$low95CI[models_mcp1$Predictor=="MCP1_rank"]),
                  high = c(models_mcp1$up95CI[models_mcp1$Predictor=="MCP1_rank"]))

fp <- ggplot(data=dat, aes(x=group, y=cen, ymin=low, ymax=high)) +
  geom_pointrange() + 
  geom_hline(yintercept=1, lty=2) +  # add a dotted line at x=1 after flip
  coord_flip() +  # flip coordinates (puts labels on y axis)
  xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
  theme(text = element_text(size=14)) +
  ggtitle("Serum MCP-1 levels (1 SD increment)") +
  theme_minimal()  # use a white background
print(fp)
rm(fp)
```

Forest plots in R (ggplot) with side table
========================================================

A friend asked me to help with a forest plot recently. After chatting about what she wanted the end result to look like, this is what I came up with.

```{r results='hide', tidy=F, echo=FALSE}
library(ggplot2)
library(gridExtra)
dat <- data.frame(group = factor(c("A","B","C","D","E","F","G"), 
                                 levels=c("F","E","D","C","B","A","G")),
                  cen = c(3.1,2.0,1.6,3.2,3.6,7.6,NA),
                  low = c(2,0.9,0.8,1.5,2,4.2,NA),
                  high = c(6,4,2,6,5,14.5,NA))

theme_set(theme_bw())

theme_update(
    axis.line = element_line(colour = "black"),
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    panel.border = element_blank(),
    panel.background = element_blank(),
    axis.text.y = element_blank(),
    axis.ticks.y = element_blank(),
    plot.margin = unit(c(0,0,0,0), "lines")
)

p <- ggplot(dat,aes(cen,group)) + 
    geom_point(size=5, shape=18) +
    geom_errorbarh(aes(xmax = high, xmin = low), height = 0.15) +
    geom_vline(xintercept = 1, linetype = "longdash") +
    scale_x_continuous(breaks = seq(0,14,1), labels = seq(0,14,1)) +
    labs(x="Adjusted Odds Ratio", y="")

lab <- data.frame(V0 = factor(c("A","B","C","D","E","F","G","A","B","C","D","E","F","G","A","B","C","D","E","F","G","A","B","C","D","E","F","G"),
                              levels=c("G","F","E","D","C","B","A")),
                  V05 = rep(c(1,2,3,4),each=7),
                  V1 = c("Occuption","Active","","Inactive","","Inactive","","Recreation","Inactive","","Active","","Inactive","","Gender","Men","Women","Men","Women","Men","Women","OR",3.1,2.0,1.6,3.2,3.6,7.6)
)

data_table <- ggplot(lab, aes(x = V05, y = V0, 
                              label = format(V1, nsmall = 1))) +
    geom_text(size = 4, hjust=0, vjust=0.5) + theme_bw() +
    geom_hline(aes(yintercept=c(6.5,7.5))) + 
    theme(panel.grid.major = element_blank(), 
          legend.position = "none",
          panel.border = element_blank(), 
          axis.text.x = element_text(colour="white"),#element_blank(),
          axis.text.y = element_blank(), 
          axis.ticks = element_line(colour="white"),#element_blank(),
          plot.margin = unit(c(0,0,0,0), "lines")) +
              labs(x="",y="") +
              coord_cartesian(xlim=c(1,4.5))
```

```{r forest_plot_1, fig.width=8, fig.height=4, tidy=F}
# grid.arrange(data_table, p, ncol=2)
library(patchwork)
data_table | p
```

Some other options for forest plots included this:



From [Abhijit](http://statbandit.wordpress.com/2010/11/01/forest-plots-using-r-and-ggplot2/) blog. But I figured I'd start a fresh, and I'd already borrowed [some code](http://mcfromnz.wordpress.com/2012/05/05/kaplan-meier-survival-plot-with-at-risk-table-by-sub-groups/) of Abhijits in the past for a survival curve.

The package [rmeta](http://cran.r-project.org/web/packages/rmeta/index.html) offers two different versions. One with forestplot like so:

```{r forest_plot_2, fig.width=6, fig.height=6, tidy=F, echo=FALSE}
library(rmeta)
data(cochrane)
steroid <- meta.MH(n.trt, n.ctrl, ev.trt, ev.ctrl,
names=name, data=cochrane)
tabletext<-cbind(c("","Study",steroid$names,NA,"Summary"),
c("Deaths","(steroid)",cochrane$ev.trt,NA,NA),
c("Deaths","(placebo)", cochrane$ev.ctrl, NA,NA),
c("","OR",format(exp(steroid$logOR),digits=2),NA,format(exp(steroid$logMH),digits=2))
)
m<- c(NA,NA,steroid$logOR,NA,steroid$logMH)
l<- m-c(NA,NA,steroid$selogOR,NA,steroid$selogMH)*2
u<- m+c(NA,NA,steroid$selogOR,NA,steroid$selogMH)*2
forestplot(tabletext,m,l,u,zero=0,is.summary=c(TRUE,TRUE,rep(FALSE,8),TRUE),
clip=c(log(0.1),log(2.5)), xlog=TRUE,
col=meta.colors(box="royalblue",line="darkblue", summary="royalblue"))
```

And one with metaplot like so:

```{r forest_plot_3, fig.width=6, fig.height=6, tidy=F, echo=FALSE}
data(catheter)
a <- meta.MH(n.trt, n.ctrl, col.trt, col.ctrl, data=catheter,
names=Name, subset=c(13,6,5,3,7,12,4,11,1,8,10,2))
metaplot(a$logOR, a$selogOR, nn=a$selogOR^-2, a$names,
summn=a$logMH, sumse=a$selogMH, sumnn=a$selogMH^-2,
logeffect=TRUE)
```

Ultimately the two would be used for different purposes, and these examples shown are straight from the [manual](http://cran.r-project.org/web/packages/rmeta/rmeta.pdf) for [rmeta](http://cran.r-project.org/web/packages/rmeta/index.html). 

It wasn't too much work to create 'another' custom option to get what my friend wanted, so thought I'd share the code for anyone else interest. There was a little bit of 'misdirection' for lack of a better term to get it across the line, and I'm sure elements of this are sloppy. You can access the code on my [github](https://github.com/nzcoops/blog_code/blob/master/forest_plot.Rmd).

I wrote this post in [RStudio](http://rstudio.org/download/) using the [R Markdown](http://www.rstudio.org/docs/authoring/using_markdown) language and then [knitr](http://yihui.name/knitr/) to turn in into markdown (.md), and then [pandoc](http://johnmacfarlane.net/pandoc/) to turn it into html. The original file is available here on [github](https://github.com/nzcoops/blog_code/blob/master/forest_plot.Rmd).

system("pandoc -s forest_plot.md -o forest_plot.html")

# Session information

------

    Version:      v1.0.6
    Last update:  2020-06-16
    Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
    Description:  Script to analyse MCP1 from the Ather-Express Biobank Study.
    Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).
    
    
    Changes log
    * v1.0.6 Only analyses and figures that end up in the final manuscript.
    * v1.0.5 Update with 30- and 90-days survival.
    * v1.0.4 Updated with Cox-regressions.
    * v1.0.3 Included more models.
    * v1.0.2 Bugs fixed.
    * v1.0.1 Extended with linear and logistic regressions
    * v1.0.0 Inital version
    

------

```{r eval = TRUE}
sessionInfo()
```

# Saving environment
```{r Saving}
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".sample_selection.RData"))
```

------
<sup>&copy; 1979-2020 Sander W. van der Laan | s.w.vanderlaan-2[at]umcutrecht.nl | [swvanderlaan.github.io](https://swvanderlaan.github.io).</sup>
------

